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