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.
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Publishing Group
2017-09-01
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| Series: | Nature Communications |
| Online Access: | http://link.springer.com/article/10.1038/s41467-017-00623-3 |