Literature DB >> 14960470

SVM based method for predicting HLA-DRB1*0401 binding peptides in an antigen sequence.

Manoj Bhasin1, G P S Raghava.   

Abstract

Prediction of peptides binding with MHC class II allele HLA-DRB1(*)0401 can effectively reduce the number of experiments required for identifying helper T cell epitopes. This paper describes support vector machine (SVM) based method developed for identifying HLA-DRB1(*)0401 binding peptides in an antigenic sequence. SVM was trained and tested on large and clean data set consisting of 567 binders and equal number of non-binders. The accuracy of the method was 86% when evaluated through 5-fold cross-validation technique.

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Year:  2004        PMID: 14960470     DOI: 10.1093/bioinformatics/btg424

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  43 in total

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Journal:  Immunogenetics       Date:  2005-05-03       Impact factor: 2.846

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Journal:  Immunogenetics       Date:  2007-12-19       Impact factor: 2.846

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7.  Francisella tularensis T-cell antigen identification using humanized HLA-DR4 transgenic mice.

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9.  Predictor for the effect of amino acid composition on CD4+ T cell epitopes preprocessing.

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Journal:  J Immunol Methods       Date:  2013-02-26       Impact factor: 2.303

10.  Prediction of HLA-DQ8beta cell peptidome using a computational program and its relationship to autoreactive T cells.

Authors:  Kuan Y Chang; Emil R Unanue
Journal:  Int Immunol       Date:  2009-05-21       Impact factor: 4.823

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