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

• Published in: Artificial intelligence in medicine Vol. 55; no. 2; pp. 107 - 115
• Main Authors: He, Ju, Yang, Guobing, Rao, Hanbing, Li, Zerong, Ding, Xianping, Chen, Yuzong
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.06.2012
• Subjects: Area Under Curve; Artificial Intelligence; Benchmarking; Binding; Binding Sites; Computer Simulation; Continuous kernel discrimination; Databases, Protein; Feature selection; Histocompatibility Antigens Class II - chemistry; Humans; Internal Medicine; Kernels; Machine learning; Major histocompatibility complex class II peptides; Mathematical models; Metropolis Monte Carlo simulated annealing; Models, Chemical; Monte Carlo Method; Monte Carlo methods; Other; Peptides; Peptides - chemistry; Protein Binding; Feature selection; Major histocompatibility complex class II peptides; Metropolis Monte Carlo simulated annealing; Continuous kernel discrimination

Accurate 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...