New tools for exploring immunogenic neoepitopes in cancer.
Identifying neoepitopes that elicit an adaptive immune response is a major bottleneck to developing personalized cancer immunotherapy therapies. Experimental validation of candidate neoepitopes is extremely resource intensive, and the vast majority of candidates are non-immunogenic, making their identification a needle-in-a-haystack problem. To address this challenge, we developed BigMHC, a deep learning method that predicts MHC-I epitopes and identifies immunogenic neoepitopes with high precision.
We develop computational methods to interpret genetic variants in people with cancer.
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