Timothy J. Sears

Predicting Neoantigen Immunogenicity from In Vivo Immune Editing

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: Enhancing Immunotherapy Response Prediction through Integration of Qualified Immunogenicity and Clonality-Aware Neoantigen Landscapes

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

Translating clinical gene sequencing into a foundational representation of tumor subtype

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.

Genetic association and machine learning improves discovery and prediction of type 1 diabetes

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.