Abstract:Accurate prediction of major histocompatibility complex (MHC)-peptide binding affinity could provide essential insights into cellular immune responses and guide the discovery of neoantigens and personalized immunotherapies. Nevertheless, the existing deep learning-based approaches for predicting MHC-II peptide interactions fall short of satisfactory performance and offer restricted model interpretability. In this study, we propose a novel deep neural network, termed ConBoTNet, to address the above issues by in… Show more
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