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Perfecting antigen prediction
Advances in genomics and precision measurement have continued to demonstrate the importance of the immune system across many disease types. At the heart of many emerging approaches to leverage these insights for precision immunotherapies is the computational antigen prediction problem. We propose a...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Rockefeller University Press
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9386507/ https://www.ncbi.nlm.nih.gov/pubmed/35972475 http://dx.doi.org/10.1084/jem.20220846 |
_version_ | 1784769827493117952 |
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author | Hoyos, David Greenbaum, Benjamin D. |
author_facet | Hoyos, David Greenbaum, Benjamin D. |
author_sort | Hoyos, David |
collection | PubMed |
description | Advances in genomics and precision measurement have continued to demonstrate the importance of the immune system across many disease types. At the heart of many emerging approaches to leverage these insights for precision immunotherapies is the computational antigen prediction problem. We propose a threefold approach to improving antigen predictions: further defining the geometry of the antigen landscape, incorporating the coupling of antigen recognition to other cellular processes, and diversifying the training sets used for models that predict immunogenicity. |
format | Online Article Text |
id | pubmed-9386507 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Rockefeller University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-93865072023-02-16 Perfecting antigen prediction Hoyos, David Greenbaum, Benjamin D. J Exp Med Viewpoint Advances in genomics and precision measurement have continued to demonstrate the importance of the immune system across many disease types. At the heart of many emerging approaches to leverage these insights for precision immunotherapies is the computational antigen prediction problem. We propose a threefold approach to improving antigen predictions: further defining the geometry of the antigen landscape, incorporating the coupling of antigen recognition to other cellular processes, and diversifying the training sets used for models that predict immunogenicity. Rockefeller University Press 2022-08-16 /pmc/articles/PMC9386507/ /pubmed/35972475 http://dx.doi.org/10.1084/jem.20220846 Text en © 2022 Hoyos and Greenbaum https://creativecommons.org/licenses/by-nc-sa/4.0/http://www.rupress.org/terms/This article is distributed under the terms of an Attribution–Noncommercial–Share Alike–No Mirror Sites license for the first six months after the publication date (see http://www.rupress.org/terms/). After six months it is available under a Creative Commons License (Attribution–Noncommercial–Share Alike 4.0 International license, as described at https://creativecommons.org/licenses/by-nc-sa/4.0/). |
spellingShingle | Viewpoint Hoyos, David Greenbaum, Benjamin D. Perfecting antigen prediction |
title | Perfecting antigen prediction |
title_full | Perfecting antigen prediction |
title_fullStr | Perfecting antigen prediction |
title_full_unstemmed | Perfecting antigen prediction |
title_short | Perfecting antigen prediction |
title_sort | perfecting antigen prediction |
topic | Viewpoint |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9386507/ https://www.ncbi.nlm.nih.gov/pubmed/35972475 http://dx.doi.org/10.1084/jem.20220846 |
work_keys_str_mv | AT hoyosdavid perfectingantigenprediction AT greenbaumbenjamind perfectingantigenprediction |