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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
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  • ORCID record for Neythen J. Treloar
  • For correspondence: neythen.treloar.14@ucl.ac.uk
Nathan Braniff
2Department of Applied Mathematics, University of Waterloo, Canada
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Brian Ingalls
2Department of Applied Mathematics, University of Waterloo, Canada
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Chris P. Barnes
1Department of Cell and Developmental Biology, University College London, London, WC1E 6BT, UK
3UCL Genetics Institute, University College London, United Kingdom
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Posted May 10, 2022.
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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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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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