Interpretable Fine‐Grained Phenotypes of Subcellular Dynamics via Unsupervised Deep Learning

Uncovering fine‐grained phenotypes of live cell dynamics is pivotal for a comprehensive understanding of the heterogeneity in healthy and diseased biological processes. However, this endeavor poses significant technical challenges for unsupervised machine learning, requiring the extraction of featur...

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Veröffentlicht in:	Advanced science Jg. 11; H. 41; S. e2403547 - n/a
Hauptverfasser:	Wang, Chuangqi, Choi, Hee June, Woodbury, Lucy, Lee, Kwonmoo
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Germany John Wiley & Sons, Inc 01.11.2024 John Wiley and Sons Inc Wiley
Schlagworte:	Cancer cell migration Cell Movement - physiology Clustering Datasets Deep Learning Humans live cell imaging Machine learning morphodynamics Neural networks Neural Networks, Computer Phenotype phenotyping Time series Unsupervised Machine Learning cell migration morphodynamics live cell imaging machine learning phenotyping
ISSN:	2198-3844, 2198-3844
Online-Zugang:	Volltext
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