Predicting HLA class II antigen presentation through integrated deep learning
Predicting HLA class II antigen presentation through integrated deep learning
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New York: Nature Publishing Group US
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Language
English
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Publisher
New York: Nature Publishing Group US
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Contents
Accurate prediction of antigen presentation by human leukocyte antigen (HLA) class II molecules would be valuable for vaccine development and cancer immunotherapies. Current computational methods trained on in vitro binding data are limited by insufficient training data and algorithmic constraints. Here we describe MARIA (major histocompatibility complex analysis with recurrent integrated architecture;
https://maria.stanford.edu/
), a multimodal recurrent neural network for predicting the likelihood of antigen presentation from a gene of interest in the context of specific HLA class II alleles. In addition to in vitro binding measurements, MARIA is trained on peptide HLA ligand sequences identified by mass spectrometry, expression levels of antigen genes and proteas...
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Full title
Predicting HLA class II antigen presentation through integrated deep learning
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TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_7075463
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_7075463
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ISSN
1087-0156,1546-1696
E-ISSN
1546-1696
DOI
10.1038/s41587-019-0280-2
