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.

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Autores principales:	Philipp Eulenberg, Niklas Köhler, Thomas Blasi, Andrew Filby, Anne E. Carpenter, Paul Rees, Fabian J. Theis, F. Alexander Wolf
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/967a554eed1a48ca89b012f67075b3c9
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spelling	oai:doaj.org-article:967a554eed1a48ca89b012f67075b3c92021-12-02T14:42:24ZReconstructing cell cycle and disease progression using deep learning10.1038/s41467-017-00623-32041-1723https://doaj.org/article/967a554eed1a48ca89b012f67075b3c92017-09-01T00:00:00Zhttps://doi.org/10.1038/s41467-017-00623-3https://doaj.org/toc/2041-1723The 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.Philipp EulenbergNiklas KöhlerThomas BlasiAndrew FilbyAnne E. CarpenterPaul ReesFabian J. TheisF. Alexander WolfNature PortfolioarticleScienceQENNature Communications, Vol 8, Iss 1, Pp 1-6 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q 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
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.
format	article
author	Philipp Eulenberg Niklas Köhler Thomas Blasi Andrew Filby Anne E. Carpenter Paul Rees Fabian J. Theis F. Alexander Wolf
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 Portfolio
publishDate	2017
url	https://doaj.org/article/967a554eed1a48ca89b012f67075b3c9
work_keys_str_mv	AT philippeulenberg reconstructingcellcycleanddiseaseprogressionusingdeeplearning AT niklaskohler reconstructingcellcycleanddiseaseprogressionusingdeeplearning AT thomasblasi reconstructingcellcycleanddiseaseprogressionusingdeeplearning AT andrewfilby reconstructingcellcycleanddiseaseprogressionusingdeeplearning AT anneecarpenter reconstructingcellcycleanddiseaseprogressionusingdeeplearning AT paulrees reconstructingcellcycleanddiseaseprogressionusingdeeplearning AT fabianjtheis reconstructingcellcycleanddiseaseprogressionusingdeeplearning AT falexanderwolf reconstructingcellcycleanddiseaseprogressionusingdeeplearning
_version_	1718389703173996544

Reconstructing cell cycle and disease progression using deep learning

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