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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 o...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9860061/ https://www.ncbi.nlm.nih.gov/pubmed/36630908 http://dx.doi.org/10.1016/j.stemcr.2022.11.007 |
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author | Ouyang, John F. Chothani, Sonia Rackham, Owen J.L. |
author_facet | Ouyang, John F. Chothani, Sonia Rackham, Owen J.L. |
author_sort | Ouyang, John F. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9860061 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-98600612023-01-22 Deep learning models will shape the future of stem cell research Ouyang, John F. Chothani, Sonia Rackham, Owen J.L. Stem Cell Reports Perspective 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. Elsevier 2023-01-10 /pmc/articles/PMC9860061/ /pubmed/36630908 http://dx.doi.org/10.1016/j.stemcr.2022.11.007 Text en © 2022 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Perspective Ouyang, John F. Chothani, Sonia Rackham, Owen J.L. Deep learning models will shape the future of stem cell research |
title | Deep learning models will shape the future of stem cell research |
title_full | Deep learning models will shape the future of stem cell research |
title_fullStr | Deep learning models will shape the future of stem cell research |
title_full_unstemmed | Deep learning models will shape the future of stem cell research |
title_short | Deep learning models will shape the future of stem cell research |
title_sort | deep learning models will shape the future of stem cell research |
topic | Perspective |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9860061/ https://www.ncbi.nlm.nih.gov/pubmed/36630908 http://dx.doi.org/10.1016/j.stemcr.2022.11.007 |
work_keys_str_mv | AT ouyangjohnf deeplearningmodelswillshapethefutureofstemcellresearch AT chothanisonia deeplearningmodelswillshapethefutureofstemcellresearch AT rackhamowenjl deeplearningmodelswillshapethefutureofstemcellresearch |