A New Kernel Based on Weighted Cross-Correlation Coefficient for SVMs and Its Application on Prediction of T-cell Epitopes

Author

Huang, Jing

Author_Institution

Sch. of Comput. Sci., Wuhan Univ.

Volume

2

fYear

0

fDate

0-0 0

Firstpage

691

Lastpage

694

Abstract

T-cell epitopes play vital roles in immune response. Its recognition by T-cell receptors is a precondition for the activation of T-cell clone. This recognition is antigen-specific. Therefore, identifying the pattern of a MHC restricted T-cell epitopes is of great importance for immunotherapy and vaccine design. In this paper, we designed a new kernel based on weighted cross-correlation coefficients for support vector machine and applied it to the direct prediction of T-cell epitopes. The experiment was carried on an MHC type I restricted T-cell clone LAU203-1.5. The results showed that this approach is efficient and promising

Keywords

biology computing; cellular biophysics; support vector machines; LAU203-1.5; SVM; T-cell clone; T-cell receptor; immune response; immunotherapy; major histocompatibility complex restricted T-cell epitopes; pattern identification; vaccine design; weighted cross-correlation coefficient; Application software; Cloning; Delay estimation; Kernel; Peptides; Proteins; Sequences; Support vector machines; Testing; Vaccines;

fLanguage

English

Publisher

ieee

Conference_Titel

Pattern Recognition, 2006. ICPR 2006. 18th International Conference on

Conference_Location

Hong Kong

ISSN

1051-4651

Print_ISBN

0-7695-2521-0

Type

conf

DOI

10.1109/ICPR.2006.120

Filename

1699299