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Deep Learning for Imaging Flow Cytometry: Cell Cycle Analysis of Jurkat Cells
Philipp Eulenberg, Niklas Köhler, Thomas Blasi, Andrew Filby, Anne E. Carpenter, Paul Rees, Fabian J. Theis, F. Alexander Wolf
doi: https://doi.org/10.1101/081364
Philipp Eulenberg
1Helmholtz Zentrum München - German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Munich, Germany.
2Department of Physics, Arnold Sommerfeld Center for Theoretical Physics, LMU München, Munich, Germany.
Niklas Köhler
1Helmholtz Zentrum München - German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Munich, Germany.
2Department of Physics, Arnold Sommerfeld Center for Theoretical Physics, LMU München, Munich, Germany.
Thomas Blasi
1Helmholtz Zentrum München - German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Munich, Germany.
Andrew Filby
3Flow Cytometry Core Facility, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK.
Anne E. Carpenter
4Imaging Platform at the Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
Paul Rees
4Imaging Platform at the Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
5College of Engineering, Swansea University, Singleton Park, Swansea, UK.
Fabian J. Theis
1Helmholtz Zentrum München - German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Munich, Germany.
6Department of Mathematics, TU München, Munich, Germany.
F. Alexander Wolf
1Helmholtz Zentrum München - German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Munich, Germany.
Article usage
Posted October 17, 2016.
Deep Learning for Imaging Flow Cytometry: Cell Cycle Analysis of Jurkat Cells
Philipp Eulenberg, Niklas Köhler, Thomas Blasi, Andrew Filby, Anne E. Carpenter, Paul Rees, Fabian J. Theis, F. Alexander Wolf
bioRxiv 081364; doi: https://doi.org/10.1101/081364
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