Timothy J. Sears

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.

NeoPrecis improves immunotherapy response prediction by integrating immunogenicity and clonality-aware neoantigen landscapes, showing better performance than tumor mutation burden in melanoma and NSCLC.

This paper introduces MutationProjector, an AI model that converts tumor mutation profiles into cancer subtype representations, achieving strong performance in predicting treatment responses and classifying metastasis.

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.

This study identifies 165 T1D risk signals and develops a machine learning model, T1GRS, that improves risk prediction for T1D in Europeans and African Americans.