mhctools/mhcflurry.py
def predict_peptides(self, peptides):
        """
        Predict MHC affinity for peptides.
        """

        # importing locally to avoid slowing down CLI applications which
        # don't use MHCflurry
        from mhcflurry.encodable_sequences import EncodableSequences

        binding_predictions = []
        encodable_sequences = EncodableSequences.create(peptides)
        for allele in self.alleles:
            predictions_df = self.predictor.predict_to_dataframe(
                encodable_sequences, allele=allele)
            for (_, row) in predictions_df.iterrows():
                binding_prediction = BindingPrediction(
                    allele=allele,
                    peptide=row.peptide,
                    affinity=row.prediction,
                    percentile_rank=(
                        row.prediction_percentile
                        if 'prediction_percentile' in row else nan),
                    prediction_method_name="mhcflurry"
                )
                binding_predictions.append(binding_prediction)
        return BindingPredictionCollection(binding_predictions)
def predict_peptides(self, peptides):
        """
        Predict MHC affinity for peptides.
        """

        # importing locally to avoid slowing down CLI applications which
        # don't use MHCflurry
        from mhcflurry.encodable_sequences import EncodableSequences

        binding_predictions = []
        encodable_sequences = EncodableSequences.create(peptides)
        for allele in self.alleles:
            predictions_df = self.predictor.predict_to_dataframe(
                encodable_sequences, allele=allele)
            for (_, row) in predictions_df.iterrows():
                binding_prediction = BindingPrediction(
                    allele=allele,
                    peptide=row.peptide,
                    affinity=row.prediction,
                    percentile_rank=(
                        row.prediction_percentile
                        if 'prediction_percentile' in row else nan),
                    prediction_method_name="mhcflurry"
                )
                binding_predictions.append(binding_prediction)
        return BindingPredictionCollection(binding_predictions)
def predict_peptides ( self , peptides ) : # importing locally to avoid slowing down CLI applications which # don't use MHCflurry from mhcflurry . encodable_sequences import EncodableSequences binding_predictions = [ ] encodable_sequences = EncodableSequences . create ( peptides ) for allele in self . alleles : predictions_df = self . predictor . predict_to_dataframe ( encodable_sequences , allele = allele ) for ( _ , row ) in predictions_df . iterrows ( ) : binding_prediction = BindingPrediction ( allele = allele , peptide = row . peptide , affinity = row . prediction , percentile_rank = ( row . prediction_percentile if 'prediction_percentile' in row else nan ) , prediction_method_name = "mhcflurry" ) binding_predictions . append ( binding_prediction ) return BindingPredictionCollection ( binding_predictions )
Predict MHC affinity for peptides.
Predict MHC affinity for peptides .