IConMHC: a deep learning convolutional neural network model to predict peptide and MHC-I binding aff...
IConMHC: a deep learning convolutional neural network model to predict peptide and MHC-I binding affinity
About this item
Full title
Author / Creator
Publisher
Berlin/Heidelberg: Springer Berlin Heidelberg
Journal title
Language
English
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Publication information
Publisher
Berlin/Heidelberg: Springer Berlin Heidelberg
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Scope and Contents
Contents
Tumor-specific neoantigens are mutated self-peptides presented by tumor cell major histocompatibility complex (MHC) molecules and are necessary to elicit host’s anti-cancer cytotoxic T cell responses. It could be specifically recognized by neoantigen-specific T cell receptors (TCRs). However, current wet-lab assays for identifying peptide MHC bindi...
Alternative Titles
Full title
IConMHC: a deep learning convolutional neural network model to predict peptide and MHC-I binding affinity
Authors, Artists and Contributors
Author / Creator
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Record Identifier
TN_cdi_proquest_miscellaneous_2416944770
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_miscellaneous_2416944770
Other Identifiers
ISSN
0093-7711
E-ISSN
1432-1211
DOI
10.1007/s00251-020-01163-9
