Fig. 2: MUNIS outperforms existing predictors in classifying HLA-I binders across 8–11mers. | Nature Machine Intelligence

Fig. 2: MUNIS outperforms existing predictors in classifying HLA-I binders across 8–11mers.

From: Deep learning enhances the prediction of HLA class I-presented CD8+ T cell epitopes in foreign pathogens

Fig. 2: MUNIS outperforms existing predictors in classifying HLA-I binders across 8–11mers.

a,b, Average precision (a) and ROC-AUC (b) of MUNIS and current state-of-the-art tools MixMHCpred 2.2, NetMHCpan 4.1, MHCflurry 2.0, TransPHLA and BigMHC on predicting eluted ligands (binders) from mass spectrometry experiments from Pyke et al.27 against decoy peptides (non-binders), n = 24 HLA-I alleles. Percentages of overlap with the training datasets of each tool across all epitopes in the presentation benchmark are shown below the plots. c, Per-allele pairwise comparisons of MUNIS and other predictors in classifying HLA-I binders. Each point is the model performance on one allele. d,e, Average precision (d) and ROC-AUC (e) of all predictors on classifying binders versus non-binders binned by epitope length, n = 24 HLA-I alleles. P values for pairwise comparisons between MUNIS and each predictor were calculated using the two-sided Wilcoxon rank sums test (not shown if P > 0.1; ****P < 1 × 10-4). Box plots are presented with medians as centre lines, 25th and 75th percentiles as lower and upper quartiles, and 1.5 times the interquartile range from the quartiles as whiskers (outliers not shown).

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