A deep generative model of 3D single-cell organization
A deep generative model of 3D single-cell organization
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Publisher
United States: Public Library of Science
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Language
English
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Publisher
United States: Public Library of Science
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Scope and Contents
Contents
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 representation of subcellular structure localization...
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Full title
A deep generative model of 3D single-cell organization
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TN_cdi_plos_journals_2762183718
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_plos_journals_2762183718
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ISSN
1553-7358,1553-734X
E-ISSN
1553-7358
DOI
10.1371/journal.pcbi.1009155
