Nature Communications (Sep 2017)

Reconstructing cell cycle and disease progression using deep learning

  • Philipp Eulenberg,
  • Niklas Köhler,
  • Thomas Blasi,
  • Andrew Filby,
  • Anne E. Carpenter,
  • Paul Rees,
  • Fabian J. Theis,
  • F. Alexander Wolf

DOI
https://doi.org/10.1038/s41467-017-00623-3
Journal volume & issue
Vol. 8, no. 1
pp. 1 – 6

Abstract

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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.