Trade-offs inside the black box of neoantigen prediction

Success of precision neoantigen-based immunotherapies hinges on the selection of immunogenic neoantigens, yet currently neither large-scale datasets nor streamlined methods are available to achieve this goal. Müller et al. present a large experimental dataset resource along with machine learning-bas...

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Vydáno v:Immunity (Cambridge, Mass.) Ročník 56; číslo 11; s. 2466
Hlavní autoři: Yao, Ning, Greenbaum, Benjamin D
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States 14.11.2023
Témata:
Antigens, Neoplasm
Humans
Immunotherapy
Machine Learning
Neoplasms
ISSN:1097-4180, 1097-4180
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Shrnutí:Success of precision neoantigen-based immunotherapies hinges on the selection of immunogenic neoantigens, yet currently neither large-scale datasets nor streamlined methods are available to achieve this goal. Müller et al. present a large experimental dataset resource along with machine learning-based models to classify immunogenic neoantigens.
Bibliografie:ObjectType-Article-1
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ObjectType-Feature-2
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ISSN:1097-4180
1097-4180
DOI:10.1016/j.immuni.2023.10.011