established methods for a scaled PhIP-seq protocol that can test 800 samples in parallel and used them to generate large datasets of antigens for APS1, IPEX, RAG1/2 deficiency, Kawasaki disease, multisystem inflammatory syndrome in children, and mild and severe forms of COVID-19 and employed machine learning to construct models that can predict disease status and individual antigens that could serve as biomarkers for these diseases. This evidence concerns the gene AIRE and Kawasaki disease.