DynaMorph: self-supervised learning of morphodynamic states of live cells

A cell’s shape and motion represent fundamental aspects of cell identity and can be highly predictive of function and pathology. However, automated analysis of the morphodynamic states remains challenging for most cell types, especially primary human cells where genetic labeling may not be feasible....

Descripción completa

Detalles Bibliográficos
Autores principales: Wu, Zhenqin, Chhun, Bryant B., Popova, Galina, Guo, Syuan-Ming, Kim, Chang N., Yeh, Li-Hao, Nowakowski, Tomasz, Zou, James, Mehta, Shalin B.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The American Society for Cell Biology 2022
Materias:
Articles
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9265147/
https://www.ncbi.nlm.nih.gov/pubmed/35138913
http://dx.doi.org/10.1091/mbc.E21-11-0561

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9265147/
https://www.ncbi.nlm.nih.gov/pubmed/35138913
http://dx.doi.org/10.1091/mbc.E21-11-0561

Ejemplares similares