Reconstructing cell cycle and disease progression using deep learning.
Nat Commun
; 8(1): 463, 2017 09 06.
Article
en En
| MEDLINE
| ID: mdl-28878212
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.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.
Texto completo:
1
Base de datos:
MEDLINE
Asunto principal:
Redes Neurales de la Computación
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Progresión de la Enfermedad
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Retinopatía Diabética
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Aprendizaje Automático
Tipo de estudio:
Prognostic_studies
Idioma:
En
Revista:
Nat Commun
Asunto de la revista:
BIOLOGIA
/
CIENCIA
Año:
2017
Tipo del documento:
Article