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Reconstructing cell cycle and disease progression using deep learning
- 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 :
- Science
Subjects
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