Systematically benchmarking peptide-MHC binding predictors: From synthetic to naturally processed epitopes.
A number of machine learning-based predictors have been developed for identifying immunogenic T-cell epitopes based on major histocompatibility complex (MHC) class I and II binding affinities. Rationally selecting the most appropriate tool has been complicated by the evolving training data and machi...
Main Authors: | Weilong Zhao, Xinwei Sher |
---|---|
Format: | Article |
Language: | English |
Published: |
Public Library of Science (PLoS)
2018-11-01
|
Series: | PLoS Computational Biology |
Online Access: | http://europepmc.org/articles/PMC6224037?pdf=render |
Similar Items
-
A community resource benchmarking predictions of peptide binding to MHC-I molecules.
by: Bjoern Peters, et al.
Published: (2006-06-01) -
MHCBN 4.0: A database of MHC/TAP binding peptides and T-cell epitopes
by: Bhasin Manoj, et al.
Published: (2009-04-01) -
Level of neo-epitope predecessor and mutation type determine T cell activation of MHC binding peptides
by: Hanan Besser, et al.
Published: (2019-05-01) -
Systematic identification of cancer-specific MHC-binding peptides with RAVEN
by: Michaela C. Baldauf, et al.
Published: (2018-09-01) -
In silico and in vivo analysis of Toxoplasma gondii epitopes by correlating survival data with peptide–MHC-I binding affinities
by: Si-Yang Huang, et al.
Published: (2016-07-01)