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Deep Reinforcement Learning for Optimal Experimental Design in Biology
View ORCID ProfileNeythen J. Treloar, Nathan Braniff, Brian Ingalls, Chris P. Barnes
doi: https://doi.org/10.1101/2022.05.09.491138
Neythen J. Treloar
1Department of Cell and Developmental Biology, University College London, London, WC1E 6BT, UK
Nathan Braniff
2Department of Applied Mathematics, University of Waterloo, Canada
Brian Ingalls
2Department of Applied Mathematics, University of Waterloo, Canada
Chris P. Barnes
1Department of Cell and Developmental Biology, University College London, London, WC1E 6BT, UK
3UCL Genetics Institute, University College London, United Kingdom
Posted May 10, 2022.
Deep Reinforcement Learning for Optimal Experimental Design in Biology
Neythen J. Treloar, Nathan Braniff, Brian Ingalls, Chris P. Barnes
bioRxiv 2022.05.09.491138; doi: https://doi.org/10.1101/2022.05.09.491138
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