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典型文献
NetBCE:An Interpretable Deep Neural Network for Accurate Prediction of Linear B-cell Epitopes
文献摘要:
Identification of B-cell epitopes(BCEs)plays an essential role in the development of pep-tide vaccines and immuno-diagnostic reagents,as well as antibody design and production.In this work,we generated a large benchmark dataset comprising 124,879 experimentally supported linear epitope-containing regions in 3567 protein clusters from over 1.3 million B cell assays.Analysis of this curated dataset showed large pathogen diversity covering 176 different families.The accuracy in linear BCE prediction was found to strongly vary with different features,while all sequence-derived and structural features were informative.To search more efficient and interpretive feature representations,a ten-layer deep learning framework for linear BCE prediction,namely NetBCE,was developed.NetBCE achieved high accuracy and robust performance with the average area under the curve(AUC)value of 0.8455 in five-fold cross-validation through automatically learning the informative classification features.NetBCE substantially outperformed the conventional ma-chine learning algorithms and other tools,with more than 22.06%improvement of AUC value com-pared to other tools using an independent dataset.Through investigating the output of important network modules in NetBCE,epitopes and non-epitopes tended to be presented in distinct regions with efficient feature representation along the network layer hierarchy.The NetBCE is freely avail-able at .
文献关键词:
作者姓名:
Haodong Xu;Zhongming Zhao
作者机构:
Center for Precision Health,School of Biomedical Informatics,The University of Texas Health Science Center at Houston,Houston,TX 77030,USA;Human Genetics Center,School of Public Health,The University of Texas Health Science Center at Houston,Houston,TX 77030,USA;The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences,Houston,TX 77030,USA;Department of Biomedical Informatics,Vanderbilt University Medical Center,Nashville,TN 37203,USA
引用格式:
[1]Haodong Xu;Zhongming Zhao-.NetBCE:An Interpretable Deep Neural Network for Accurate Prediction of Linear B-cell Epitopes)[J].基因组蛋白质组与生物信息学报(英文版),2022(05):1002-1012
A类:
NetBCE,Epitopes
B类:
Interpretable,Deep,Neural,Network,Accurate,Prediction,Linear,cell,Identification,epitopes,BCEs,plays,essential,role,development,pep,tide,vaccines,immuno,diagnostic,reagents,well,antibody,design,production,this,generated,large,benchmark,dataset,comprising,experimentally,supported,linear,containing,regions,protein,clusters,from,million,assays,Analysis,curated,showed,pathogen,diversity,covering,different,families,accuracy,prediction,was,found,strongly,vary,features,while,sequence,derived,structural,were,informative,To,search,more,efficient,interpretive,representations,layer,deep,learning,framework,namely,developed,achieved,high,robust,performance,average,area,under,curve,value,five,fold,cross,validation,through,automatically,classification,substantially,outperformed,conventional,chine,algorithms,other,tools,than,improvement,pared,using,independent,Through,investigating,output,important,network,modules,tended,presented,distinct,along,hierarchy,freely,avail
AB值:
0.59235
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