DeepCycle reconstructs a cyclic cell cycle trajectory from unsegmented cell images using convolutional neural networks-Reference-Cited by-同舟云学术

DeepCycle reconstructs a cyclic cell cycle trajectory from unsegmented cell images using convolutional neural networks

Published:2020-10 Issue:10 Volume:16 Page:
ISSN:1744-4292
Container-title:Molecular Systems Biology
language:en
Short-container-title:Mol Syst Biol

Author:

Rappez Luca¹²^ORCID,Rakhlin Alexander³^ORCID,Rigopoulos Angelos¹^ORCID,Nikolenko Sergey⁴⁵^ORCID,Alexandrov Theodore¹⁶^ORCID

Affiliation:

1. Structural and Computational Biology Unit European Molecular Biology Laboratory Heidelberg Germany

2. Faculty of Biosciences Collaboration for joint PhD degree between EMBL and Heidelberg University Heidelberg Germany

3. Neuromation OU Tallinn Estonia

4. National Research Institute Higher School of Economics St. Petersburg Russia

5. Steklov Institute of Mathematics at St. Petersburg St. Petersburg Russia

6. Skaggs School of Pharmacy and Pharmaceutical Sciences University of California San Diego La Jolla CA USA

Publisher

EMBO

Subject

Applied Mathematics,Computational Theory and Mathematics,General Agricultural and Biological Sciences,General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,Information Systems

Link

https://onlinelibrary.wiley.com/doi/pdf/10.15252/msb.20209474

Reference15 articles.

1. Local cellular neighborhood controls proliferation in cell competition;Bove A;Mol Biol Cell,2017

2. Driving the Cell Cycle Through Metabolism

3. Reconstructing cell cycle and disease progression using deep learning

4. Trajectories of cell-cycle progression from fixed cell populations

5. Cell cycle, CDKs and cancer: a changing paradigm

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