A deep generative model of 3D single-cell organization

We introduce a framework for end-to-end integrative modeling of 3D single-cell multi-channel fluorescent image data of diverse subcellular structures. We employ stacked conditional β-variational autoencoders to first learn a latent representation of cell morphology, and then learn a latent represent...

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Detalles Bibliográficos
Autores principales: Donovan-Maiye, Rory M., Brown, Jackson M., Chan, Caleb K., Ding, Liya, Yan, Calysta, Gaudreault, Nathalie, Theriot, Julie A., Maleckar, Mary M., Knijnenburg, Theo A., Johnson, Gregory R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8797242/
https://www.ncbi.nlm.nih.gov/pubmed/35041651
http://dx.doi.org/10.1371/journal.pcbi.1009155

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8797242/
https://www.ncbi.nlm.nih.gov/pubmed/35041651
http://dx.doi.org/10.1371/journal.pcbi.1009155

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