title: A probabilistic meta-predictor for the MHC class II binding peptides.
abstract: Several computational methods for the prediction of major histocompatibility complex (MHC) class II binding peptides embodying different strengths and weaknesses have been developed. To provide reliable prediction, it is important to design a system that enables the integration of outcomes from various predictors. The construction of a meta-predictor of this type based on a probabilistic approach is introduced in this paper. The design permits the easy incorporation of results obtained from any number of individual predictors. It is demonstrated that this integrated method outperforms six state-of-the-art individual predictors based on computational studies using MHC class II peptides from 13 HLA alleles and three mouse MHC alleles obtained from the Immune Epitope Database and Analysis Resource. It is concluded that this integrative approach provides a clearly enhanced reliability of prediction. Moreover, this computational framework can be directly extended to MHC class I binding predictions.
authors: Department of Bioengineering (MC063), University of Illinois at Chicago, 851 South Morgan Street, Chicago, IL 60607, USA. okarpe2@uic.edu
authors: Oleksiy
authors: O
authors: Karpenko
authors: Lei
authors: L
authors: Huang
authors: Yang
authors: Y
authors: Dai
mesh_terms: D000465:Algorithms; D000818:Animals; D019295:Computational Biology / Q000379:methods*; D016384:Consensus Sequence / Q000276:immunology; D006681:HLA-D Antigens / Q000737:chemistry / Q000276:immunology / Q000378:metabolism*; D006801:Humans; D051379:Mice; D018448:Models, Immunological; D010455:Peptides / Q000276:immunology* / Q000378:metabolism*; D011237:Predictive Value of Tests; D011336:Probability; D011485:Protein Binding / Q000276:immunology
chemical_list: D006681:HLA-D Antigens; D010455:Peptides