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Reconstructing cell cycle and disease progression using deep learning

Authors :
Philipp Eulenberg
Niklas Köhler
Thomas Blasi
Andrew Filby
Anne E. Carpenter
Paul Rees
Fabian J. Theis
F. Alexander Wolf
Source :
Nature Communications, Vol 8, Iss 1, Pp 1-6 (2017)
Publication Year :
2017
Publisher :
Nature Portfolio, 2017.

Abstract

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.

Subjects

Subjects :
Science

Details

Language :
English
ISSN :
20411723
Volume :
8
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Nature Communications
Publication Type :
Academic Journal
Accession number :
edsdoj.967a554eed1a48ca89b012f67075b3c9
Document Type :
article
Full Text :
https://doi.org/10.1038/s41467-017-00623-3