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Deep Neural Networks Predict MHC-I Epitope Presentation and Transfer Learn Neoepitope Immunogenicity

View ORCID ProfileBenjamin Alexander Albert, Yunxiao Yang, Xiaoshan M. Shao, Dipika Singh, Kellie N. Smith, Valsamo Anagnostou, View ORCID ProfileRachel Karchin
doi: https://doi.org/10.1101/2022.08.29.505690
Benjamin Alexander Albert
1Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
2Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA
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  • ORCID record for Benjamin Alexander Albert
Yunxiao Yang
1Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
2Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA
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Xiaoshan M. Shao
1Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
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Dipika Singh
3The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
4Bloomberg~Kimmel Institute for Cancer Immunotherapy, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
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Kellie N. Smith
3The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
4Bloomberg~Kimmel Institute for Cancer Immunotherapy, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
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Valsamo Anagnostou
3The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
4Bloomberg~Kimmel Institute for Cancer Immunotherapy, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
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Rachel Karchin
1Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
2Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA
3The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
5Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, USA
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  • ORCID record for Rachel Karchin
  • For correspondence: karchin@jhu.edu
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Article Information

doi 
https://doi.org/10.1101/2022.08.29.505690
History 
  • January 18, 2023.

Article Versions

  • Version 1 (August 29, 2022 - 20:08).
  • You are currently viewing Version 2 of this article (January 18, 2023 - 20:24).
  • View Version 3, the most recent version of this article.
Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

Author Information

  1. Benjamin Alexander Albert1,2,
  2. Yunxiao Yang1,2,
  3. Xiaoshan M. Shao1,
  4. Dipika Singh3,4,
  5. Kellie N. Smith3,4,
  6. Valsamo Anagnostou3,4 and
  7. Rachel Karchin1,2,3,5,*
  1. 1Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
  2. 2Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA
  3. 3The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
  4. 4Bloomberg~Kimmel Institute for Cancer Immunotherapy, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
  5. 5Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, USA
  1. ↵*Corresponding Author, Rachel Karchin, Johns Hopkins University, 217A Hackerman Hall, 3400 North Charles Street, Baltimore, MD 21218, Phone: 410-516-5578, Fax: 410-516-5294, karchin{at}jhu.edu
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Posted January 18, 2023.
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Deep Neural Networks Predict MHC-I Epitope Presentation and Transfer Learn Neoepitope Immunogenicity
Benjamin Alexander Albert, Yunxiao Yang, Xiaoshan M. Shao, Dipika Singh, Kellie N. Smith, Valsamo Anagnostou, Rachel Karchin
bioRxiv 2022.08.29.505690; doi: https://doi.org/10.1101/2022.08.29.505690
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Deep Neural Networks Predict MHC-I Epitope Presentation and Transfer Learn Neoepitope Immunogenicity
Benjamin Alexander Albert, Yunxiao Yang, Xiaoshan M. Shao, Dipika Singh, Kellie N. Smith, Valsamo Anagnostou, Rachel Karchin
bioRxiv 2022.08.29.505690; doi: https://doi.org/10.1101/2022.08.29.505690

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