Cargando…
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....
| Autores principales: | , , , , , , , , |
|---|---|
| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
The American Society for Cell Biology
2022
|
| Materias: | |
| 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 |