Robust classification of cell cycle phase and biological feature extraction by image-based deep learning-Reference-Cited by-同舟云学术

Robust classification of cell cycle phase and biological feature extraction by image-based deep learning

Published:2020-06-15 Issue:13 Volume:31 Page:1346-1354
ISSN:1059-1524
Container-title:Molecular Biology of the Cell
language:en
Short-container-title:MBoC

Author:

Nagao Yukiko¹,Sakamoto Mika²,Chinen Takumi¹,Okada Yasushi³⁴⁵,Takao Daisuke³

Affiliation:

1. Faculty of Pharmaceutical Sciences, The University of Tokyo, Tokyo 113-0033, Japan

2. Genome Informatics Laboratory, National Institute of Genetics, Mishima 411-8540, Japan

3. Department of Cell Biology and Anatomy and International Research Center for Neurointelligence (WPI-IRCN), Graduate School of Medicine, The University of Tokyo, Tokyo 113-0033, Japan

4. Department of Physics and Universal Biology Institute (UBI), Graduate School of Science, The University of Tokyo, Tokyo 113-0033, Japan

5. Laboratory for Cell Polarity Regulation, Center for Biosystems Dynamics Research (BDR), RIKEN, Osaka 565-0874, Japan

Abstract

By applying convolutional neural network-based classifiers, we demonstrate that cell images can be robustly classified according to cell cycle phases. Combined with Grad-CAM analysis, our approach enables us to extract biological features underlying cellular phenomena of interest in an unbiased and data-driven manner.

Publisher

American Society for Cell Biology (ASCB)

Subject

Cell Biology,Molecular Biology

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