典型文献
Predicting Microbe-Disease Association Based on Heterogeneous Network and Global Graph Feature Learning
文献摘要：
Numerous microbes inhabit human body,making a vast difference in human health.Hence,discov-ering associations between microbes and diseases is bene-ficial 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 similarities.Based on microbe Gaussian interaction profile(GIP)ker-nel similarity,we consider different effects of these mi-crobes on organs in the human body to further improve microbe similarity.For disease similarity network,we combine GIP kernel similarity,disease semantic similar-ity and disease-symptom similarity.And then,an embed-ding algorithm called GraRep is used to learn global structural information for this network.According to vec-tor feature of every node,we utilize support vector ma-chine classifier to calculate the score for each microbe-dis-ease pair.HNGFL achieves a reliable performance in cross validation,outperforming the compared methods.In addi-tion,we carry out case studies of three diseases.Results show that HNGFL can be considered as a reliable meth-od for microbe-disease association prediction.
文献关键词：
中图分类号：
作者姓名：
WANG Yueyue;LEI Xiujuan;PAN Yi
作者机构：
School of Computer Science,Shaanxi Normal University,Xi'an 710119,China;Department of Computer Science,Georgia State University,Atlanta,GA 30302,USA;Faculty of Computer Science and Control Engineering,Shenzhen Institute of Advanced Technology,Chinese Academy of Sciences,Shenzhen 518055,China
文献出处：
引用格式：
[1]WANG Yueyue;LEI Xiujuan;PAN Yi-.Predicting Microbe-Disease Association Based on Heterogeneous Network and Global Graph Feature Learning)[J].电子学报（英文）,2022(02):345-353
A类：
Microbe,HNGFL,crobes,GraRep
B类：
Predicting,Disease,Association,Based,Heterogeneous,Network,Global,Graph,Feature,Learning,Numerous,microbes,inhabit,human,body,making,vast,difference,health,Hence,discov,ering,associations,between,diseases,bene,ficial,prevention,treatment,In,this,study,develop,prediction,by,learning,global,graph,feature,heterogeneous,network,called,Firstly,integrated,known,multiple,similarities,Gaussian,interaction,profile,GIP,similarity,different,effects,these,organs,further,improve,For,combine,kernel,semantic,symptom,And,then,embed,algorithm,used,structural,information,According,every,node,utilize,support,vector,chine,classifier,calculate,score,each,pair,achieves,reliable,performance,cross,validation,outperforming,compared,methods,addi,carry,case,studies,three,Results,show,that,can,considered
AB值：
0.528346
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