Kairi Tanaka

This paper introduces NEMo, a machine learning model that predicts neoantigen immunogenicity using in vivo immune editing data, showing improved stratification of progression-free survival across multiple clinical settings.

This paper uses machine learning to predict ICB response by integrating germline and somatic features, revealing differences in immune pathways and clinical outcomes between MHC-I and MHC-II neoantigen reliant tumors.