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...
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| Veröffentlicht in: | The Journal of experimental medicine Jg. 219; H. 9 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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United States
05.09.2022
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| ISSN: | 1540-9538, 1540-9538 |
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| Abstract | 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. |
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| AbstractList | 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.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. 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. |
| Author | Hoyos, David Greenbaum, Benjamin D |
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| CitedBy_id | crossref_primary_10_1016_j_intimp_2024_112811 crossref_primary_10_1021_acsinfecdis_4c01053 crossref_primary_10_3389_fimmu_2023_1238586 |
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| Title | Perfecting antigen prediction |
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