A computer-implemented method for training a machine learning algorithm or statistical inference model to identify peptides that contain features positively associated with natural endogenous or exogenous cellular processing, transportation and major histocompatibility complex, MHC, presentation, that negates the influence of HLA/MHC-binding, once trained, and can be applied to any peptide regardless of its MHC restriction, the method comprising: (a) building one or more training data sets comprising a positive and a negative data set; wherein the positive data set comprises entries of peptide sequences identified or inferred from surface bound or secreted HLA/MHC/peptide complexes encoded by one or a plurality of different HLA/MHC alleles, and wherein the negative data set comprises entries of peptide sequences which are not identified or inferred from surface bound or secreted HLA/MHC/peptide complexes; characterised in that the training data further comprises a multiplicity of pairings between entries of the positive and negative data sets; and wherein each pair of said multiplicity of pairings comprises peptide sequences which: (i) are of equal or similar length, and (ii) are derived from the same source protein or fragment thereof, and (iii) have similar binding affinities, with respect to the HLA/MHC molecule to which the positive counterpart is restricted, and (b) applying a machine learning algorithm or statistical inference model on said training data, wherein, following step (b), the trained machine learning algorithm or statistical inference model can be used to interrogate input data comprising amino acid sequences of peptides and/or proteins with said machine learning model, to identify peptides, or peptide fragments of said proteins, having features positively associated with natural endogenous or exogenous cellular processing, transportation and HLA/MHC presentation. (a) building one or more training data sets comprising a positive and a negative data set; wherein the positive data set comprises entries of peptide sequences identified or inferred from surface bound or secreted HLA/MHC/peptide complexes encoded by one or a plurality of different HLA/MHC alleles, and wherein the negative data set comprises entries of peptide sequences which are not identified or inferred from surface bound or secreted HLA/MHC/peptide complexes; characterised in that the training data further comprises a multiplicity of pairings between entries of the positive and negative data sets; and wherein each pair of said multiplicity of pairings comprises peptide sequences which: (i) are of equal or similar length, and (ii) are derived from the same source protein or fragment thereof, and (iii) have similar binding affinities, with respect to the HLA/MHC molecule to which the positive counterpart is restricted, wherein the positive data set comprises entries of peptide sequences identified or inferred from surface bound or secreted HLA/MHC/peptide complexes encoded by one or a plurality of different HLA/MHC alleles, and wherein the negative data set comprises entries of peptide sequences which are not identified or inferred from surface bound or secreted HLA/MHC/peptide complexes; characterised in that the training data further comprises a multiplicity of pairings between entries of the positive and negative data sets; and wherein each pair of said multiplicity of pairings comprises peptide sequences which: (i) are of equal or similar length, and (ii) are derived from the same source protein or fragment thereof, and (iii) have similar binding affinities, with respect to the HLA/MHC molecule to which the positive counterpart is restricted, (i) are of equal or similar length, and (ii) are derived from the same source protein or fragment thereof, and (iii) have similar binding affinities, with respect to the HLA/MHC molecule to which the positive counterpart is restricted, and (b) applying a machine learning algorithm or statistical inference model on said training data, wherein, following step (b), the trained machine learning algorithm or statistical inference model can be used to interrogate input data comprising amino acid sequences of peptides and/or proteins with said machine learning model, to identify peptides, or peptide fragments of said proteins, having features positively associated with natural endogenous or exogenous cellular processing, transportation and HLA/MHC presentation.