… Moreover, the antigenic peptides have variable lengths, … a kernel method that can handle
variable length peptides … In this paper, we present a kernel method based on the direct kernel …

… string kernel MHC2SK (MHC-II String Kernel) method to measure the similarities among
peptides with variable lengths. By considering the distinct features of MHC-II peptide binding …

… peptides and major histocompatibility complex class I (MHC I) proteins, although our methods
can be readily applicable to other types of peptide… method to predict promising peptides for …

… peptide identification methods, we propose a method to predict if a peptide is naturally
processed by the MHC … Therefore, such kernels alleviate the need for considering peptides of …

… of our method on the MHC–peptide binding benchmark … kernels, the non-linear poly peptide
kernel outperforms the baseline linseq kernel, which confirms that linear models based on …

… based method (SVMHC) to predict peptides that bind MHC … space, meaning that different
kernels will represent the input … The kernels tested for MHC class I peptide predictions were …

… exact binding affinity of the peptide. As a kernel-based approach, SVRMHC demonstrates
the excellent modeling performance enjoyed by other SVM-based methods such as SVMHC […

… approaches to improve the predictive power of kernelbased Machine Learning methods for
MHC … -based vaccine, the prediction of peptides binding to MHC-I is of great interest in the …

… However, not all MHC II binding peptides has such a region or the … kernel based method
for predicting MHC II peptides. This method is competitive to handle various lengths of peptides (…

… We propose a new machine learning approach based on kernel methods [14] … kernel is
shown to be a suitable similarity measure between peptides and pseudo-sequences of MHC…