Computer-implemented method for determining presentation likelihoods of a set of neoantigens by a tumour cell of a tumour of a subject, the method comprising the steps of: - obtaining at least one of exome or whole genome nucleotide sequencing data and transcriptome nucleotide sequencing data from tumour cells associated to said tumour and normal cells of the subject; - obtaining a set of aberrant genomic events associated to said tumour by comparing the exome and/or whole genome nucleotide sequencing data and the transcriptome nucleotide sequencing data from the tumour cells to the exome and/or whole genome nucleotide sequencing data and the transcriptome nucleotide sequencing data from the normal cells; - obtaining data representing peptide sequences of each of a set of neoantigens identified based at least in part on said set of aberrant events, wherein the peptide sequence of each neoantigen comprises at least one alteration which makes it distinct from a corresponding wild-type peptide sequence identified from the normal cells of the subject; - obtaining data representing a peptide sequence of a human leukocyte antigen (HLA) based on the tumour exome and/or whole genome nucleotide sequencing data and the transcriptome nucleotide sequencing data from the tumour cells; - training a deep learning model on a training data set comprising a positive data set, wherein the positive data set comprises a plurality of input-output pairs, wherein each pair comprises an entry of an epitope sequence as input, said epitope sequence being identified or inferred from a surface bound or secreted HLA/peptide complex encoded by a corresponding HLA allele expressed by a training cell, wherein each pair further comprises an entry of a peptide sequence of an alpha-chain encoded by the corresponding HLA allele as output; and - determining a presentation likelihood for each of the set of neoantigens for the peptide sequence of the HLA by means of the trained model. - obtaining at least one of exome or whole genome nucleotide sequencing data and transcriptome nucleotide sequencing data from tumour cells associated to said tumour and normal cells of the subject; - obtaining a set of aberrant genomic events associated to said tumour by comparing the exome and/or whole genome nucleotide sequencing data and the transcriptome nucleotide sequencing data from the tumour cells to the exome and/or whole genome nucleotide sequencing data and the transcriptome nucleotide sequencing data from the normal cells; - obtaining data representing peptide sequences of each of a set of neoantigens identified based at least in part on said set of aberrant events, wherein the peptide sequence of each neoantigen comprises at least one alteration which makes it distinct from a corresponding wild-type peptide sequence identified from the normal cells of the subject; - obtaining data representing a peptide sequence of a human leukocyte antigen (HLA) based on the tumour exome and/or whole genome nucleotide sequencing data and the transcriptome nucleotide sequencing data from the tumour cells; - training a deep learning model on a training data set comprising a positive data set, wherein the positive data set comprises a plurality of input-output pairs, wherein each pair comprises an entry of an epitope sequence as input, said epitope sequence being identified or inferred from a surface bound or secreted HLA/peptide complex encoded by a corresponding HLA allele expressed by a training cell, wherein each pair further comprises an entry of a peptide sequence of an alpha-chain encoded by the corresponding HLA allele as output; and - determining a presentation likelihood for each of the set of neoantigens for the peptide sequence of the HLA by means of the trained model. <EOS>