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.

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Auteurs principaux: Philipp Eulenberg, Niklas Köhler, Thomas Blasi, Andrew Filby, Anne E. Carpenter, Paul Rees, Fabian J. Theis, F. Alexander Wolf
Format: article
Langue:EN
Publié: Nature Portfolio 2017
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Accès en ligne:https://doaj.org/article/967a554eed1a48ca89b012f67075b3c9
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