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Wonseok Jeon

Alumni

Publications

Circulating proteins to predict COVID-19 severity
Chen-Yang Su
Sirui Zhou
Edgar Gonzalez-Kozlova
Guillaume Butler-Laporte
Elsa Brunet-Ratnasingham
Tomoko Nakanishi
Wonseok Jeon
David R. Morrison
Laetitia Laurent
Jonathan Afilalo
Marc Afilalo
Danielle Henry
Yiheng Chen
Julia Carrasco-Zanini
Yossi Farjoun
Maik Pietzner
Nofar Kimchi
Zaman Afrasiabi
Nardin Rezk
Meriem Bouab … (voir 43 de plus)
Louis Petitjean
Charlotte Guzman
Xiaoqing Xue
Chris Tselios
Branka Vulesevic
Olumide Adeleye
Tala Abdullah
Noor Almamlouk
Yara Moussa
Chantal DeLuca
Naomi Duggan
Erwin Schurr
Diane Marie Del Valle
Ryan Thompson
Mario A. Cedillo
Eric Schadt
Kai Nie
Nicole W. Simons
Konstantinos Mouskas
Nicolas Zaki
Manishkumar Patel
Hui Xie
Jocelyn Harris
Robert Marvin
Esther Cheng
Kevin Tuballes
Kimberly Argueta
Ieisha Scott
Celia M. T. Greenwood
Clare Paterson
Michael A. Hinterberg
Claudia Langenberg
Vincenzo Forgetta
Joelle Pineau
Vincent Mooser
Thomas Marron
Noam D. Beckmann
Seunghee Kim-schulze
Alexander W. Charney
Sacha Gnjatic
Daniel E. Kaufmann
Miriam Merad
J. Brent Richards
Predicting COVID-19 severity is difficult, and the biological pathways involved are not fully understood. To approach this problem, we measu… (voir plus)red 4701 circulating human protein abundances in two independent cohorts totaling 986 individuals. We then trained prediction models including protein abundances and clinical risk factors to predict COVID-19 severity in 417 subjects and tested these models in a separate cohort of 569 individuals. For severe COVID-19, a baseline model including age and sex provided an area under the receiver operator curve (AUC) of 65% in the test cohort. Selecting 92 proteins from the 4701 unique protein abundances improved the AUC to 88% in the training cohort, which remained relatively stable in the testing cohort at 86%, suggesting good generalizability. Proteins selected from different COVID-19 severity were enriched for cytokine and cytokine receptors, but more than half of the enriched pathways were not immune-related. Taken together, these findings suggest that circulating proteins measured at early stages of disease progression are reasonably accurate predictors of COVID-19 severity. Further research is needed to understand how to incorporate protein measurement into clinical care.
2023-04-16
Scientific Reports (publié)
doi.org
Circulating proteins to predict adverse COVID-19 outcomes
Chen-Yang Su
Sirui Zhou
Edgar Gonzalez-Kozlova
Guillaume Butler-Laporte
Elsa Brunet-Ratnasingham
Tomoko Nakanishi
Wonseok Jeon
David Morrison
Laetitia Laurent
Jonathan Afilalo
Marc Afilalo
Danielle Henry
Yiheng Chen
Julia Carrasco-Zanini
Yossi Farjoun
Maik Pietzner
Nofar Kimchi
Zaman Afrasiabi
Nardin Rezk
Meriem Bouab … (voir 47 de plus)
Louis Petitjean
Charlotte Guzman
Xiaoqing Xue
Chris Tselios
Branka Vulesevic
Olumide Adeleye
Tala Abdullah
Noor Almamlouk
Yara Moussa
Chantal DeLuca
Naomi Duggan
Erwin Schurr
Nathalie Brassard
Madeleine Durand
Diane Marie Del Valle
Ryan Thompson
Mario A. Cedillo
Eric Schadt
Kai Nie
Nicole W Simons
Konstantinos Mouskas
Nicolas Zaki
Manishkumar Patel
Hui Xie
Jocelyn Harris
Robert Marvin
Esther Cheng
Kevin Tuballes
Kimberly Argueta
Ieisha Scott
Celia M T Greenwood
Clare Paterson
Michael A. Hinterberg
Claudia Langenberg
Vincenzo Forgetta
Joelle Pineau
Vincent Mooser
Thomas Marron
Noam Beckmann
Ephraim Kenigsberg
Seunghee Kim-schulze
Alexander W. Charney
Sacha Gnjatic
Daniel E. Kaufmann
Miriam Merad
J Brent Richards
J Brent Richards
Predicting COVID-19 severity is difficult, and the biological pathways involved are not fully understood. To approach this problem, we measu… (voir plus)red 4,701 circulating human protein abundances in two independent cohorts totaling 986 individuals. We then trained prediction models including protein abundances and clinical risk factors to predict adverse COVID-19 outcomes in 417 subjects and tested these models in a separate cohort of 569 individuals. For severe COVID-19, a baseline model including age and sex provided an area under the receiver operator curve (AUC) of 65% in the test cohort. Selecting 92 proteins from the 4,701 unique protein abundances improved the AUC to 88% in the training cohort, which remained relatively stable in the testing cohort at 86%, suggesting good generalizability. Proteins selected from different adverse COVID-19 outcomes were enriched for cytokine and cytokine receptors, but more than half of the enriched pathways were not immune-related. Taken together, these findings suggest that circulating proteins measured at early stages of disease progression are reasonably accurate predictors of adverse COVID-19 outcomes. Further research is needed to understand how to incorporate protein measurement into clinical care.
2021-10-04
medRxiv (prépublication)
doi.org
Adversarial Soft Advantage Fitting: Imitation Learning without Policy Optimization
Paul Barde
Julien Roy
Wonseok Jeon
Joelle Pineau
Christopher Pal
Derek Nowrouzezahrai
Adversarial Imitation Learning alternates between learning a discriminator -- which tells apart expert's demonstrations from generated ones … (voir plus)-- and a generator's policy to produce trajectories that can fool this discriminator. This alternated optimization is known to be delicate in practice since it compounds unstable adversarial training with brittle and sample-inefficient reinforcement learning. We propose to remove the burden of the policy optimization steps by leveraging a novel discriminator formulation. Specifically, our discriminator is explicitly conditioned on two policies: the one from the previous generator's iteration and a learnable policy. When optimized, this discriminator directly learns the optimal generator's policy. Consequently, our discriminator's update solves the generator's optimization problem for free: learning a policy that imitates the expert does not require an additional optimization loop. This formulation effectively cuts by half the implementation and computational burden of Adversarial Imitation Learning algorithms by removing the Reinforcement Learning phase altogether. We show on a variety of tasks that our simpler approach is competitive to prevalent Imitation Learning methods.
2019-12-31
Advances in Neural Information Processing Systems 33 (NeurIPS 2020) (publié)
doi.org
arxiv.org