Reconstructing cell cycle and disease progression using deep learning

Reconstructing cell cycle and disease progression using deep learning

The interpretation of information-rich, high-throughput single-cell data is a challenge requiring sophisticated computational tools. Here the authors demonstrate a deep convolutional neural network that can classify cell cycle status on-the-fly.

Bibliographic Details
Main Authors: Philipp Eulenberg, Niklas Köhler, Thomas Blasi, Andrew Filby, Anne E. Carpenter, Paul Rees, Fabian J. Theis, F. Alexander Wolf
Format: Article
Language:English
Published: Nature Publishing Group 2017-09-01
Series:Nature Communications
Online Access:http://link.springer.com/article/10.1038/s41467-017-00623-3
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spelling doaj-art-84ab3244feda4d7887c609a87464639f2018-09-09T11:16:55ZengNature Publishing GroupNature Communications2041-17232017-09-01811610.1038/s41467-017-00623-3Reconstructing cell cycle and disease progression using deep learningPhilipp Eulenberg0Niklas Köhler1Thomas Blasi2Andrew Filby3Anne E. Carpenter4Paul Rees5Fabian J. Theis6F. Alexander Wolf7Helmholtz Zentrum München—German Research Center for Environmental Health, Institute of Computational BiologyHelmholtz Zentrum München—German Research Center for Environmental Health, Institute of Computational BiologyHelmholtz Zentrum München—German Research Center for Environmental Health, Institute of Computational BiologyFlow Cytometry Core Facility, Faculty of Medical Sciences, Newcastle UniversityImaging Platform at the Broad Institute of Harvard and Massachusetts Institute of TechnologyImaging Platform at the Broad Institute of Harvard and Massachusetts Institute of TechnologyHelmholtz Zentrum München—German Research Center for Environmental Health, Institute of Computational BiologyHelmholtz Zentrum München—German Research Center for Environmental Health, Institute of Computational BiologyThe interpretation of information-rich, high-throughput single-cell data is a challenge requiring sophisticated computational tools. Here the authors demonstrate a deep convolutional neural network that can classify cell cycle status on-the-fly.http://link.springer.com/article/10.1038/s41467-017-00623-3
institution Open Data Bank
collection Open Access Journals
building Directory of Open Access Journals
language English
format Article
author Philipp Eulenberg
Niklas Köhler
Thomas Blasi
Andrew Filby
Anne E. Carpenter
Paul Rees
Fabian J. Theis
F. Alexander Wolf
spellingShingle Philipp Eulenberg
Niklas Köhler
Thomas Blasi
Andrew Filby
Anne E. Carpenter
Paul Rees
Fabian J. Theis
F. Alexander Wolf
Reconstructing cell cycle and disease progression using deep learning
Nature Communications
author_facet Philipp Eulenberg
Niklas Köhler
Thomas Blasi
Andrew Filby
Anne E. Carpenter
Paul Rees
Fabian J. Theis
F. Alexander Wolf
author_sort Philipp Eulenberg
title Reconstructing cell cycle and disease progression using deep learning
title_short Reconstructing cell cycle and disease progression using deep learning
title_full Reconstructing cell cycle and disease progression using deep learning
title_fullStr Reconstructing cell cycle and disease progression using deep learning
title_full_unstemmed Reconstructing cell cycle and disease progression using deep learning
title_sort reconstructing cell cycle and disease progression using deep learning
publisher Nature Publishing Group
series Nature Communications
issn 2041-1723
publishDate 2017-09-01
description The interpretation of information-rich, high-throughput single-cell data is a challenge requiring sophisticated computational tools. Here the authors demonstrate a deep convolutional neural network that can classify cell cycle status on-the-fly.
url http://link.springer.com/article/10.1038/s41467-017-00623-3
_version_ 1612609951145918464

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