… Combining the threading approach with a machine learning philosophy of fitting to data,
we show that it is possible to estimate a pairwise potential matrix and also learn additional …

… to predict peptide-MHC binding, and a number of methods, trained using results of binding
… We evaluated several recent methods to predict peptide-MHC Class I binding affinities and …

… We propose to use Multiple Kernel Learning (MKL) [9] to learn the weights βs,k of the
individual components (see Equation 11) along with the respective classifiers. In particular, we …

… Some of them employ matrix models or machine learning models based on the sequence
characteristic embedded in peptides or MHC to predict the binding ability of peptides to MHC. …

… that is able to learn peptide–MHC-I binding models for many … the MHC–peptide binding
benchmark recently proposed by Peters et al. (2006) who gathered quantitative peptide-binding …

… Machine learning models trained on sequence data exist for peptide:MHC (p:MHC) binding
… descriptor sets to predict peptide binding to a well-studied model mouse MHC I allele, H-2D …

… determinants of MHC-II motifs and revealed a widespread reverse-binding mode in HLA-DP …
-learning framework to accurately predict binding specificities and ligands of any MHC-II …

… An important shortcoming of the early machine learning prediction methods for MHC class
I peptide binding was that they (as most other early machine learning methods) required the …

… : they load short peptide fragments derived from extracellular … the peptide binds lying more
or less flat in the MHC groove, … great majority of peptides bind to the MHC using this canonical …

… methods for predicting MHC-peptide binding that improve the … methods for predicting
MHC-peptide binding and discuss … Learning regression models for predicting the binding affinity …