Computational prediction of neoantigens: do we need more data or new approaches?

Aron Charles Eklund, Zoltan Imre Szallasi

Research output: Contribution to journalEditorialResearchpeer-review

Abstract

Personalized cancer immunotherapy may benefit from improved computational algorithms for identifying neoantigens. Recent results demonstrate that machine learning can improve accuracy. Additional improvements may require more genomic data paired with in vitro T cell reactivity measurements, and more sophisticated algorithms that take into account T cell receptor specificity.
Original languageEnglish
JournalAnnals of Oncology
Volume29
Issue number4
Pages (from-to)799-800
Number of pages2
ISSN0923-7534
DOIs
Publication statusPublished - 2018

Cite this

Eklund, Aron Charles ; Szallasi, Zoltan Imre. / Computational prediction of neoantigens: do we need more data or new approaches?. In: Annals of Oncology. 2018 ; Vol. 29, No. 4. pp. 799-800.
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abstract = "Personalized cancer immunotherapy may benefit from improved computational algorithms for identifying neoantigens. Recent results demonstrate that machine learning can improve accuracy. Additional improvements may require more genomic data paired with in vitro T cell reactivity measurements, and more sophisticated algorithms that take into account T cell receptor specificity.",
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Computational prediction of neoantigens: do we need more data or new approaches? / Eklund, Aron Charles; Szallasi, Zoltan Imre.

In: Annals of Oncology, Vol. 29, No. 4, 2018, p. 799-800.

Research output: Contribution to journalEditorialResearchpeer-review

TY - JOUR

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AU - Eklund, Aron Charles

AU - Szallasi, Zoltan Imre

PY - 2018

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AB - Personalized cancer immunotherapy may benefit from improved computational algorithms for identifying neoantigens. Recent results demonstrate that machine learning can improve accuracy. Additional improvements may require more genomic data paired with in vitro T cell reactivity measurements, and more sophisticated algorithms that take into account T cell receptor specificity.

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DO - 10.1093/annonc/mdy070

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JO - Annals of Oncology

JF - Annals of Oncology

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