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Computational analysis of B cell receptor repertoires in COVID-19 patients using deep embedded representations of protein sequences

Inyoung Kim, Sang Yoon Byun, Sangyeup Kim, Sangyoon Choi, Jinsung Noh, Junho Chung, Byung Gee Kim
doi: https://doi.org/10.1101/2021.08.02.454701
Inyoung Kim
1Artificial Intelligence Institute, Seoul National University, Seoul, Republic of Korea
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  • For correspondence: iykim@snu.ac.kr
Sang Yoon Byun
2Department of Computer Science, Grinnell College, Grinnell, IA, United States of America
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Sangyeup Kim
3Department of Applied Biology and Chemistry, Seoul National University, Seoul, Republic of Korea
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Sangyoon Choi
4College of Veterinary Medicine, Seoul National University, Seoul, Republic of Korea
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Jinsung Noh
1Artificial Intelligence Institute, Seoul National University, Seoul, Republic of Korea
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Junho Chung
6Department of Biomedical Science, Seoul National University College of Medicine, Seoul, Republic of Korea
7Department of Biochemistry and Molecular Biology, Seoul National University College of Medicine, Seoul, Republic of Korea
8Cancer Research Institute, Seoul National University College of Medicine, Seoul, Republic of Korea
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Byung Gee Kim
1Artificial Intelligence Institute, Seoul National University, Seoul, Republic of Korea
5School of Chemical and Biological Engineering, Seoul National University, Seoul, Republic of Korea
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Posted November 29, 2021.
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Computational analysis of B cell receptor repertoires in COVID-19 patients using deep embedded representations of protein sequences
Inyoung Kim, Sang Yoon Byun, Sangyeup Kim, Sangyoon Choi, Jinsung Noh, Junho Chung, Byung Gee Kim
bioRxiv 2021.08.02.454701; doi: https://doi.org/10.1101/2021.08.02.454701
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Computational analysis of B cell receptor repertoires in COVID-19 patients using deep embedded representations of protein sequences
Inyoung Kim, Sang Yoon Byun, Sangyeup Kim, Sangyoon Choi, Jinsung Noh, Junho Chung, Byung Gee Kim
bioRxiv 2021.08.02.454701; doi: https://doi.org/10.1101/2021.08.02.454701

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