Uncovering Interpretable Fine-Grained Phenotypes of Subcellular Dynamics through Unsupervised Self-Training of Deep Neural Networks

Live cell imaging provides unparallel insights into dynamic cellular processes across spatiotemporal scales. Despite its potential, the inherent spatiotemporal heterogeneity within live cell imaging data often obscures critical mechanical details underlying cellular dynamics. Uncovering fine-grained...

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Vydané v:	bioRxiv
Hlavní autori:	Wang, Chuangqi, Choi, Hee June, Woodbury, Lucy, Lee, Kwonmoo
Médium:	Paper
Jazyk:	English
Vydavateľské údaje:	Cold Spring Harbor Cold Spring Harbor Laboratory Press 13.01.2024 Cold Spring Harbor Laboratory
Vydanie:	1.2
Predmet:	Bioinformatics Cytoskeleton Epithelial cells Fluorescence microscopy Learning algorithms Long short-term memory Machine learning Molecular modelling Neural networks Phenotypes Phenotyping Temporal variations
ISSN:	2692-8205, 2692-8205
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