A method of identifiying a subset of patients for treatment, comprising: obtaining, for each patient, peptide sequences of each of a set of neoantigens from tumor cells of the patient; generating, for each patient, a set of numerical presentation likelihoods for the set of neoantigens for the patient by inputting the peptide sequences of each of the set of neoantigens into a machine-learned presentation model, each presentation likelihood representing the likelihood that a corresponding neoantigen is presented by one or more MHC alleles on the surface of the tumor cells of the patient, wherein the machine-learned presentation model comprises: a plurality of parameters identified at least based on a training data set comprising: labels obtained by mass spectrometry measuring presence of peptides bound to at least one MHC allele identified as present in at least one of a plurality of samples, training peptide sequences, and at least one MHC allele associated with the training peptide sequences; and a function representing a relation between the peptide sequences and the presentation likelihoods based on the plurality of parameters; identifying, for each patient, one or more neoantigens from the set of neoantigens for the patient, wherein identifying the one or more neoantigens for the patient comprises selecting a subset of neoantigens in the set of neoantigens for the patient and wherein the subset of neoantigens are neoantigens having highest presentation likelihoods in the set of presentation likelihoods for the patient; determining, for each patient, a utility score indicating an estimated number of neoantigens presented on the surface of the tumor cells of the patient as determined by the corresponding presentation likelihoods for the one or more neoantigens for the patient; and selecting the subset of patients for treatment, each patient in the subset of patients associated with a utility score that satisfies a predetermined inclusion criteria. a plurality of parameters identified at least based on a training data set comprising: labels obtained by mass spectrometry measuring presence of peptides bound to at least one MHC allele identified as present in at least one of a plurality of samples, training peptide sequences, and at least one MHC allele associated with the training peptide sequences; and labels obtained by mass spectrometry measuring presence of peptides bound to at least one MHC allele identified as present in at least one of a plurality of samples, training peptide sequences, and at least one MHC allele associated with the training peptide sequences; and a function representing a relation between the peptide sequences and the presentation likelihoods based on the plurality of parameters; identifying, for each patient, one or more neoantigens from the set of neoantigens for the patient, wherein identifying the one or more neoantigens for the patient comprises selecting a subset of neoantigens in the set of neoantigens for the patient and wherein the subset of neoantigens are neoantigens having highest presentation likelihoods in the set of presentation likelihoods for the patient; determining, for each patient, a utility score indicating an estimated number of neoantigens presented on the surface of the tumor cells of the patient as determined by the corresponding presentation likelihoods for the one or more neoantigens for the patient; and selecting the subset of patients for treatment, each patient in the subset of patients associated with a utility score that satisfies a predetermined inclusion criteria.