IMPROVE: a feature model to predict neoepitope immunogenicity through broad-scale validation of T-cell recognition

IMPROVE: a feature model to predict neoepitope immunogenicity through broad-scale validation of T-cell recognition

BackgroundMutation-derived neoantigens are critical targets for tumor rejection in cancer immunotherapy, and better tools for neoepitope identification and prediction are needed to improve neoepitope targeting strategies. Computational tools have enabled the identification of patient-specific neoant...

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Bibliographic Details
Main Authors: Annie Borch, Ibel Carri, Birkir Reynisson, Heli M. Garcia Alvarez, Kamilla K. Munk, Alessandro Montemurro, Nikolaj Pagh Kristensen, Siri A. Tvingsholm, Jeppe Sejerø Holm, Christina Heeke, Keith Henry Moss, Ulla Kring Hansen, Anna-Lisa Schaap-Johansen, Frederik Otzen Bagger, Vinicius Araujo Barbosa de Lima, Kristoffer S. Rohrberg, Samuel A. Funt, Marco Donia, Inge Marie Svane, Ulrik Lassen, Carolina Barra, Morten Nielsen, Sine Reker Hadrup
Format: Article
Language:English
Published: Frontiers Media S.A. 2024-04-01
Series:Frontiers in Immunology
Subjects:
neoantigen
neoepitope prediction
machine learning
immunotherapy
immunoinformatics
Online Access:https://www.frontiersin.org/articles/10.3389/fimmu.2024.1360281/full

Internet

https://www.frontiersin.org/articles/10.3389/fimmu.2024.1360281/full

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