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Deep learning-enhanced morphological profiling predicts cell fate dynamics in real-time in hPSCs
Edward Ren, Sungmin Kim, Saad Mohamad, Samuel F. Huguet, Yulin Shi, Andrew R. Cohen, View ORCID ProfileEugenia Piddini, View ORCID ProfileRafael Carazo Salas
doi: https://doi.org/10.1101/2021.07.31.454574
Edward Ren
1School of Cellular & Molecular Medicine, University of Bristol, BS8 1TD Bristol, UK
Sungmin Kim
1School of Cellular & Molecular Medicine, University of Bristol, BS8 1TD Bristol, UK
Saad Mohamad
1School of Cellular & Molecular Medicine, University of Bristol, BS8 1TD Bristol, UK
Samuel F. Huguet
1School of Cellular & Molecular Medicine, University of Bristol, BS8 1TD Bristol, UK
Yulin Shi
1School of Cellular & Molecular Medicine, University of Bristol, BS8 1TD Bristol, UK
Andrew R. Cohen
2Department of Electrical and Computer Engineering, Drexel University, 3120-40 Market Street, Suite 313, Philadelphia, PA 19104, USA.
Eugenia Piddini
1School of Cellular & Molecular Medicine, University of Bristol, BS8 1TD Bristol, UK
Rafael Carazo Salas
1School of Cellular & Molecular Medicine, University of Bristol, BS8 1TD Bristol, UK

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Posted August 01, 2021.
Deep learning-enhanced morphological profiling predicts cell fate dynamics in real-time in hPSCs
Edward Ren, Sungmin Kim, Saad Mohamad, Samuel F. Huguet, Yulin Shi, Andrew R. Cohen, Eugenia Piddini, Rafael Carazo Salas
bioRxiv 2021.07.31.454574; doi: https://doi.org/10.1101/2021.07.31.454574
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