Towards the in silico identification of class II restricted T-cell epitopes: a partial least squares iterative self-consistent algorithm for affinity prediction | Zendy

Irini Doytchinova | Zendy; Darren R. Flower | Zendy

Open Access

Towards the in silico identification of class II restricted T-cell epitopes: a partial least squares iterative self-consistent algorithm for affinity prediction

Author(s) -

Irini Doytchinova,

Darren R. Flower

Publication year - 2003

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/btg312

Subject(s) - in silico , identification (biology) , class (philosophy) , partial least squares regression , algorithm , epitope , computer science , least squares function approximation , mathematics , artificial intelligence , machine learning , statistics , chemistry , biology , genetics , antigen , biochemistry , gene , botany , estimator

The immunogenicity of peptides depends on their ability to bind to MHC molecules. MHC binding affinity prediction methods can save significant amounts of experimental work. The class II MHC binding site is open at both ends, making epitope prediction difficult because of the multiple binding ability of long peptides.

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