Benchmarking Machine-Learning Classifiers for Neoantigen Immunogenicity Prediction and the Limits of SHAP Interpretability

arXiv:2611.00907v1 Announce Type: new Abstract: Personalized cancer vaccines require prioritizing, among hundreds of candidate mutant peptides, the few that will induce a T-cell response. This study asks what general-purpose machine learning achieves on that task, and whether the features such models identify as important can be trusted. We make three contributions. The first is a benchmark of sixty models: twelve supervised classifier families, each in five configurations, evaluated on the Immunogenic Tumor Specific Neoantigen database (ITSNdb; 199 experimentally validated neoantigens, 129 immunogenic and 70 non-immunogenic). All sixty are compared under one stratified k-fold cross-validation protocol, in which the data used to choose a configuration are never the data used to score it. The second is an interpretability study that combines SHAP with feature ablation. The third is a shuffled-label control that tests whether a SHAP value has been earned. Two results follow, and both run against current practice. First, the classical linear models lead the ranking. The gradient-boosted ensembles and neural networks that dominate recent work bring no advantage. Second, SHAP can fail o