Deep learning models will shape the future of stem cell research
Deep learning models will shape the future of stem cell research
Our ability to understand and control stem cell biology is being augmented by developments on two fronts, our ability to collect more data describing cell state and our capability to comprehend these data using deep learning models. Here we consider the impact deep learning will have in the future of stem cell research. We explore the importance of generating data suitable for these methods, the requirement for close collaboration between experimental and computational researchers, and the challenges we face to do this fairly and effectively. Achieving this will ensure that the resulting deep learning models are biologically meaningful and computationally tractable.
6-12
Ouyang, John F.
ce6f93a5-b40f-4add-8d7b-3ae795c1a4cb
Chothani, Sonia
24850611-01f3-46ae-af99-8c2693e6ca8f
Rackham, Owen J.L.
8122eb1f-6e9f-4da5-90e1-ce108ccbbcbf
10 January 2023
Ouyang, John F.
ce6f93a5-b40f-4add-8d7b-3ae795c1a4cb
Chothani, Sonia
24850611-01f3-46ae-af99-8c2693e6ca8f
Rackham, Owen J.L.
8122eb1f-6e9f-4da5-90e1-ce108ccbbcbf
Ouyang, John F., Chothani, Sonia and Rackham, Owen J.L.
(2023)
Deep learning models will shape the future of stem cell research.
Stem Cell Reports, 18 (1), .
(doi:10.1016/j.stemcr.2022.11.007).
Abstract
Our ability to understand and control stem cell biology is being augmented by developments on two fronts, our ability to collect more data describing cell state and our capability to comprehend these data using deep learning models. Here we consider the impact deep learning will have in the future of stem cell research. We explore the importance of generating data suitable for these methods, the requirement for close collaboration between experimental and computational researchers, and the challenges we face to do this fairly and effectively. Achieving this will ensure that the resulting deep learning models are biologically meaningful and computationally tractable.
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e-pub ahead of print date: 10 January 2023
Published date: 10 January 2023
Identifiers
Local EPrints ID: 502683
URI: http://eprints.soton.ac.uk/id/eprint/502683
ISSN: 2213-6711
PURE UUID: 34409555-84fa-43c0-b80d-53c255ee5d6e
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Date deposited: 04 Jul 2025 16:39
Last modified: 22 Aug 2025 02:30
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Author:
John F. Ouyang
Author:
Sonia Chothani
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