Reconstructing cell cycle and disease progression using deep learning

Eulenberg P, Koehler N, Blasi T, Filby A, Carpenter AE, Rees P, Theis FJ, Wolf FA (2017)

Publication Type: Journal article

Publication year: 2017

Journal

Nature Communications Nature Publishing Group: Nature Communications

Book Volume: 8

Article Number: 463

Journal Issue: 1

DOI: 10.1038/s41467-017-00623-3

Abstract

We show that deep convolutional neural networks combined with nonlinear dimension reduction enable reconstructing biological processes based on raw image data. We demonstrate this by reconstructing the cell cycle of Jurkat cells and disease progression in diabetic retinopathy. In further analysis of Jurkat cells, we detect and separate a subpopulation of dead cells in an unsupervised manner and, in classifying discrete cell cycle stages, we reach a sixfold reduction in error rate compared to a recent approach based on boosting on image features. In contrast to previous methods, deep learning based predictions are fast enough for on-the-fly analysis in an imaging flow cytometer.

Involved external institutions

Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (HMGU) / Helmholtz Munich DE Germany (DE) Eli and Edythe L. Broad Institute of MIT and Harvard US United States (USA) (US) Newcastle University GB United Kingdom (GB)

How to cite

APA:

Eulenberg, P., Koehler, N., Blasi, T., Filby, A., Carpenter, A.E., Rees, P.,... Wolf, F.A. (2017). Reconstructing cell cycle and disease progression using deep learning. Nature Communications, 8(1). https://doi.org/10.1038/s41467-017-00623-3

MLA:

Eulenberg, Philipp, et al. "Reconstructing cell cycle and disease progression using deep learning." Nature Communications 8.1 (2017).

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