Trade-offs inside the black box of neoantigen prediction Editorial

  


Authors: Yao, N.; Greenbaum, B. D.
Title: Trade-offs inside the black box of neoantigen prediction
Abstract: 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. © 2023 Elsevier Inc.
Keywords: note; prediction; immunotherapy; machine learning
Journal Title: Immunity
Volume: 56
Issue: 11
ISSN: 1074-7613
Publisher: Cell Press  
Date Published: 2023-11-14
Start Page: 2466
End Page: 2468
Language: English
DOI: 10.1016/j.immuni.2023.10.011
PROVIDER: scopus
PUBMED: 37967528
DOI/URL:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85176102467&doi=10.1016%2fj.immuni.2023.10.011&partnerID=40&md5=194b5fc9d1a7c1a3512baf7427c66072
Notes: The MSK Cancer Center Support Grant (P30 CA008748) is acknowledged in the PDF -- Corresponding author is MSK author: Benjamin D. Greenbaum -- Source: Scopus
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MSK Authors
  1. Benjamin Dylan Greenbaum
    58 Greenbaum
  2. Ning Yao
    2 Yao
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