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...
| Main Authors: | , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Elsevier BV
2020
|
| Online Access: | https://hdl.handle.net/1721.1/128919 |