Quantification of Uncertainty in Peptide-MHC Binding Prediction Improves High-Affinity Peptide Selection for Therapeutic Design

Quantification of Uncertainty in Peptide-MHC Binding Prediction Improves High-Affinity Peptide Selection for Therapeutic Design

The computational identification of peptides that can bind the major histocompatibility complex (MHC) with high affinity is an essential step in developing personal immunotherapies and vaccines. We introduce PUFFIN, a deep residual network-based computational approach that quantifies uncertainty in...

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Bibliographic Details
Main Authors:	Zeng, Haoyang, Gifford, David K
Other Authors:	Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Format:	Article
Language:	English
Published:	Elsevier BV 2020
Online Access:	https://hdl.handle.net/1721.1/128919

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