Title :
A New Kernel Based on Weighted Cross-Correlation Coefficient for SVMs and Its Application on Prediction of T-cell Epitopes
Author_Institution :
Sch. of Comput. Sci., Wuhan Univ.
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;
Conference_Titel :
Pattern Recognition, 2006. ICPR 2006. 18th International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
0-7695-2521-0
DOI :
10.1109/ICPR.2006.120
