HLA-C and colorectal carcinoma: It means distinct predictors are proficient for each MHC allele, and the model's input is the peptide of interest.[47] In contrast, NetMHCpan utilizes a “pan‐allele” method in which a single model takes both the peptide and an MHC allele representation as input.[48] With two‐output neural networks, NetMHCpan 4.0 generates predictions for binding affinity and the likelihood of mass spectrometry identification using peptides eluted from MHC and identified by mass spectrometry in its training set.[44] In CRC, these methods can be used for comparing immunopeptidomics outcomes.