A meta-predictor for MHC class II binding peptides based on Naïve 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 Naïve 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.
Huang
Lei
L
Department of Bioengineering, University of Illinois at Chicago, Chicago, IL 60607, USA. lhuang@uic.edu
Karpenko
Oleksiy
O
Murugan
Naveen
N
Dai
Yang
Y
D000483:Alleles; D000596:Amino Acids / Q000737:chemistry; D001499:Bayes Theorem; D019295:Computational Biology / Q000379:methods; D030562:Databases, Protein; D000949:Histocompatibility Antigens Class II; D006801:Humans; D015233:Models, Statistical; D008962:Models, Theoretical; D009842:Oligopeptides; D010455:Peptides / Q000737:chemistry; D011485:Protein Binding; D011506:Proteins / Q000737:chemistry; D020539:Sequence Analysis, Protein; D012984:Software
D000596:Amino Acids; D000949:Histocompatibility Antigens Class II; C408902:MHC binding peptide; D009842:Oligopeptides; D010455:Peptides; D011506:Proteins