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

Manoj Bhasin | Zendy; Gajendra P. S. Raghava | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

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

Author(s) -

Manoj Bhasin,

Gajendra P. S. Raghava

Publication year - 2004

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btg424

Subject(s) - human leukocyte antigen , support vector machine , epitope , computer science , computational biology , sequence (biology) , major histocompatibility complex , set (abstract data type) , mhc class i , antigen , artificial intelligence , biology , genetics , programming language

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.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research