Building a meta-predictor for MHC class II-binding peptides.
Prediction of class II major histocompatibility complex (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 chapter, the procedure of building such a meta-predictor based on Naïve Bayesian approach is introduced. 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.
Huang
Lei
L
Bioengineering Bioinformatics, University of Illinois at Chicago, USA.
Karpenko
Oleksiy
O
Murugan
Naveen
N
Dai
Yang
Y
D000483:Alleles; D001499:Bayes Theorem; D019295:Computational Biology; D003198:Computer Simulation; D000939:Epitopes / Q000737:chemistry / Q000378:metabolism; D006684:HLA-DR Antigens / Q000737:chemistry / Q000235:genetics / Q000378:metabolism; D059811:HLA-DRB1 Chains; D000949:Histocompatibility Antigens Class II / Q000737:chemistry / Q000378:metabolism*; D006801:Humans; D007125:Immunogenetics; D008285:Major Histocompatibility Complex; D008958:Models, Molecular; D010455:Peptides / Q000737:chemistry* / Q000276:immunology / Q000378:metabolism*; D011485:Protein Binding
D000939:Epitopes; D006684:HLA-DR Antigens; D059811:HLA-DRB1 Chains; D000949:Histocompatibility Antigens Class II; D010455:Peptides