Sensitive quantitative predictions of peptide-MHC binding by a 'Query by Committee' artificial neural network approach.

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Computational Immunology Meets Bioinformatics: The Use of Prediction Tools for Molecular Binding in the Simulation of the Immune System (2010, background): “Iterative training based on QBC-selected peptides considerably increases the sensitivity without compromising the efficiency of the predictions [75].”
Fundamentals and Methods for T- and B-Cell Epitope Prediction (2017, methodology): “Likewise, ANNs were also applied first to the prediction of peptide binding to MHC I [48, 49] and later to MHC II molecules [50] (Table 1).”
NetMHCpan, a method for MHC class I binding prediction beyond humans (2008, neutral): “…allelespecific MHC class I binding prediction methods are limited to alleles that are characterized by peptide-binding data (Brusic et al. 1994; Buus et al. 2003; Donnes and Elofsson 2002; Lundegaard et al. 2008; Mamitsuka 1998; Nielsen et al. 2003; Segal et al. 2001; Tenzer et al. 2005).”
NetMHCpan, a Method for Quantitative Predictions of Peptide Binding to Any HLA-A and -B Locus Protein of Known Sequence (2007, background): “Artificial Neural Networks (ANN) are ideally suited to recognize non-linear patterns, which are believed to contribute to peptide-HLA-I interactions [15,16,21,22].”