3D single-cell shape analysis using geometric deep learning

Matt De Vries; Lucas Dent; Nathan Curry; Leo Rowe-Brown; Vicky Bousgouni; Adam Tyson; Christopher Dunsby; Chris Bakal

doi:10.1101/2022.06.17.496550

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3D single-cell shape analysis using geometric deep learning

View ORCID ProfileMatt De Vries, View ORCID ProfileLucas Dent, View ORCID ProfileNathan Curry, View ORCID ProfileLeo Rowe-Brown, View ORCID ProfileVicky Bousgouni, View ORCID ProfileAdam Tyson, View ORCID ProfileChristopher Dunsby, View ORCID ProfileChris Bakal

doi: https://doi.org/10.1101/2022.06.17.496550

Matt De Vries

1Dynamical Cell Systems, Department of Cancer Biology, Institute of Cancer Research, London, SW3 6JB, UK

2Photonics Group, Department of Physics, Imperial College London, London, SW7 2A, UK

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Lucas Dent

1Dynamical Cell Systems, Department of Cancer Biology, Institute of Cancer Research, London, SW3 6JB, UK

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Nathan Curry

2Photonics Group, Department of Physics, Imperial College London, London, SW7 2A, UK

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Leo Rowe-Brown

2Photonics Group, Department of Physics, Imperial College London, London, SW7 2A, UK

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Vicky Bousgouni

1Dynamical Cell Systems, Department of Cancer Biology, Institute of Cancer Research, London, SW3 6JB, UK

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Adam Tyson

3Sainsbury Wellcome Centre & Gatsby Computational Neuroscience Unit, University College London, London, W1T 4JG, UK

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Christopher Dunsby

2Photonics Group, Department of Physics, Imperial College London, London, SW7 2A, UK

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Chris Bakal

1Dynamical Cell Systems, Department of Cancer Biology, Institute of Cancer Research, London, SW3 6JB, UK

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Posted March 27, 2023.

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3D single-cell shape analysis using geometric deep learning

Matt De Vries, Lucas Dent, Nathan Curry, Leo Rowe-Brown, Vicky Bousgouni, Adam Tyson, Christopher Dunsby, Chris Bakal

bioRxiv 2022.06.17.496550; doi: https://doi.org/10.1101/2022.06.17.496550

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3D single-cell shape analysis using geometric deep learning

Matt De Vries, Lucas Dent, Nathan Curry, Leo Rowe-Brown, Vicky Bousgouni, Adam Tyson, Christopher Dunsby, Chris Bakal

bioRxiv 2022.06.17.496550; doi: https://doi.org/10.1101/2022.06.17.496550

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