A Meta-predictor for MHC Class II Binding Peptides Based on Naive Bayesian Approach

Prediction of class II MHC-peptide binding is a challenging task due to variable length of binding peptides. Different computational methods have been developed; however, each has its own strength and weakness. In order to provide reliable prediction, it is important to design a system that enables the integration of outcomes from various predictors. In this paper, we introduce a procedure of building such a meta-predictor based on naive Bayesian approach. The system is designed in such a way that results obtained from any number of individual predictors can be easily incorporated. This meta-predictor is expected to give users more confidence in the prediction