Prediction of human major histocompatibility complex class II binding peptides by continuous kernel discrimination method

摘要

ObjectiveAccurate prediction of major histocompatibility complex (MHC) class II binding peptides helps reducing the experimental cost for identifying helper T cell epitopes, which has been a challenging problem partly because of the variable length of the binding peptides. This work is to develop an accurate model for predicting MHC-binding peptides using machine learning methods.