An Improved Method for Predicting Linear B-cell Epitope Using Deep Maxout Networks

查看全文 作　　者：LIAN [1]Yao;HUANG Ze [2]Chi;GE [3]Meng;PAN Xian [1]Ming

机构地区：[1]The Key Laboratory of Bioinformatics,Ministry of Education,School of Life Sciences,Tsinghua University;[2]Key Laboratory of Protein and Peptide Pharmaceutical,Institute of Biophysics,Chinese Academy of Sciences;[3]CAS Key Laboratory of Genome Sciences and Information,Beijing Institute of Genomics,Chinese Academy of Sciences

出　　处：《Biomedical and Environmental Sciences》索引2015年第28卷第6期,共4页

基　　金：supported by grant 2009CB918801 from the Ministry of Science and Technology of China 摘　　要：To establish a relation between an protein amino acid sequence and its tendencies to generate antibody response,and to investigate an improved in silico method for linear B-cell epitope(LBE)prediction.We present a sequence-based LBE predictor developed using deep maxout network(DMN)with dropout training techniques.A graphics processing unit(GPU)was used to reduce the training time of the model.A 10-fold cross-validation test on a large,non-redundant 关键词：B细胞表位表位预测网络线性氨基酸序列图形处理单元抗体反应训练技术

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An Improved Method for Predicting Linear B-cell Epitope Using Deep Maxout Networks

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