Systematically benchmarking peptide-MHC binding predictors: From synthetic to naturally processed ep...
Systematically benchmarking peptide-MHC binding predictors: From synthetic to naturally processed epitopes
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United States: Public Library of Science
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Language
English
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United States: Public Library of Science
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Contents
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 machine learning methods. Despite the recent advances m...
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Full title
Systematically benchmarking peptide-MHC binding predictors: From synthetic to naturally processed epitopes
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TN_cdi_plos_journals_2250635261
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_plos_journals_2250635261
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ISSN
1553-7358,1553-734X
E-ISSN
1553-7358
DOI
10.1371/journal.pcbi.1006457
