CTL epitopes predicted using QM, SVM, and ANN techniques.

2004 NOV 3 - (NewsRx.com & NewsRx.net) -- Researchers can predict CTL epitopes using quantitative matrix (QM), support vector machine (SVM), and artificial neural network (ANN)techniques.

"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 and machine learning techniques such as support vector machine and artificial neural network," scientists in India report.

"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," said Manoj Bhasin and G. P. S. Raghava at the Institute of Microbial Technology. "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 were nearly equal. Finally, both machine-learning methods were used for consensus and combined prediction of CTL ...