Discovering the hidden messages within cell trajectories using a deep learning approach for in vitro evaluation of cancer drug treatments

We describe a novel method to achieve a universal, massive, and fully automated analysis of cell motility behaviours, starting from time-lapse microscopy images. The approach was inspired by the recent successes in application of machine learning for style recognition in paintings and artistic style...

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Detalles Bibliográficos
Autores principales: Mencattini, A., Di Giuseppe, D., Comes, M. C., Casti, P., Corsi, F., Bertani, F. R., Ghibelli, L., Businaro, L., Di Natale, C., Parrini, M. C., Martinelli, E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7203117/
https://www.ncbi.nlm.nih.gov/pubmed/32376840
http://dx.doi.org/10.1038/s41598-020-64246-3

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7203117/
https://www.ncbi.nlm.nih.gov/pubmed/32376840
http://dx.doi.org/10.1038/s41598-020-64246-3

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