Nuclear morphology is a deep learning biomarker of cellular senescence

Cellular senescence is an important factor in aging and many age-related diseases, but understanding its role in health is challenging due to the lack of exclusive or universal markers. Using neural networks, we predict senescence from the nuclear morphology of human fibroblasts with up to 95% accur...

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Detalles Bibliográficos
Autores principales: Heckenbach, Indra, Mkrtchyan, Garik V., Ezra, Michael Ben, Bakula, Daniela, Madsen, Jakob Sture, Nielsen, Malte Hasle, Oró, Denise, Osborne, Brenna, Covarrubias, Anthony J, Idda, M. Laura, Gorospe, Myriam, Mortensen, Laust, Verdin, Eric, Westendorp, Rudi, Scheibye-Knudsen, Morten
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group US 2022
Materias:
Technical Report
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10154217/
https://www.ncbi.nlm.nih.gov/pubmed/37118134
http://dx.doi.org/10.1038/s43587-022-00263-3

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10154217/
https://www.ncbi.nlm.nih.gov/pubmed/37118134
http://dx.doi.org/10.1038/s43587-022-00263-3

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