TY  - JOUR
ID  - open203
UR  - http://www.sciencedirect.com/science/article/pii/S0264410X04001409
IS  - 23-24
A1  - Bhasin, Manoj
A1  - Raghava, G.P.S.
N2  - Cytotoxic T lymphocyte (CTL) epitopes are potential candidates for subunit vaccine design for various diseases. Most of the existing T cell epitope prediction methods are indirect methods that predict MHC class I binders instead of CTL epitopes. In this study, a systematic attempt has been made to develop a direct method for predicting CTL epitopes from an antigenic sequence. This method is based on quantitative matrix (QM) and machine learning techniques such as Support Vector Machine (SVM) and Artificial Neural Network (ANN). This method has been trained and tested on non-redundant dataset of T cell epitopes and non-epitopes that includes 1137 experimentally proven MHC class I restricted T cell epitopes. The accuracy of QM-, ANN- and SVM-based methods was 70.0, 72.2 and 75.2%, respectively. The performance of these methods has been evaluated through Leave One Out Cross-Validation (LOOCV) at a cutoff score where sensitivity and specificity was nearly equal. Finally, both machine-learning methods were used for consensus and combined prediction of CTL epitopes. The performances of these methods were evaluated on blind dataset where machine learning-based methods perform better than QM-based method. We also demonstrated through subgroup analysis that our methods can discriminate between T-cell epitopes and MHC binders (non-epitopes). In brief this method allows prediction of CTL epitopes using QM, SVM, ANN approaches. The method also facilitates prediction of MHC restriction in predicted T cell epitopes.
VL  - 22
TI  - Prediction of CTL epitopes using QM, SVM and ANN techniques.
AV  - restricted
EP  - 204
N1  - Copyright of this article belongs to Elsevier Science.
Y1  - 2004/08/13/
PB  - Elsevier Science
JF  - Vaccine
KW  - Artificial Neural Network; Support Vector Machine; LOOCV; CTL epitopes
SN  - 0264-410X
SP  - 3195
ER  -