ArticleMolecular biology of the cell2020
Robust classification of cell cycle phase and biological feature extraction by image-based deep learning.
Article in Molecular biology of the cell, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed, 48 citations in OpenAlex.
- Data modeling the interplay between single-cell shape, single-cell protein expression, and tissue state.Cell reports methods · 2026Article
- High-content phenotyping reveals Golgi dynamics and their role in cell cycle regulation.The Journal of cell biology · 2026Article
- Technological advances in visualizing and rewiring microtubules during plant development.Journal of experimental botany · 2025Review
- A deep learning approach for time-consistent cell cycle phase prediction from microscopy data.PLoS computational biology · 2025Article
- Classification of first embryonic division stages of multiple Caenorhabditis species by deep learning.NPJ systems biology and applications · 2025Article
- Supervised and unsupervised deep learning-based approaches for studying DNA replication spatiotemporal dynamics.Communications biology · 2025Article
- The role of primary cilia in myoblast proliferation and cell cycle regulation during myogenesis.Cell structure and function · 2025Review
- AI-based classification of anticancer drugs reveals nucleolar condensation as a predictor of immunogenicity.Molecular cancer · 2024Article
- Computational methods for biofabrication in tissue engineering and regenerative medicine - a literature review.Computational and structural biotechnology journal · 2024Review
- Cell identity revealed by precise cell cycle state mapping links data modalities.bioRxiv : the preprint server for biology · 2024Article
- AI analysis of super-resolution microscopy: Biological discovery in the absence of ground truth.The Journal of cell biology · 2024Review
- Applying interpretable machine learning in computational biology-pitfalls, recommendations and opportunities for new developments.Nature methods · 2024Review
- Image-based discrimination of the early stages of mesenchymal stem cell differentiation.Molecular biology of the cell · 2024Article
- Deep learning unlocks label-free viability assessment of cancer spheroids in microfluidics.Lab on a chip · 2024Article
- Metrics reloaded: recommendations for image analysis validation.Nature methods · 2024Review
- Classification of scanning electron microscope images of pharmaceutical excipients using deep convolutional neural networks with transfer learning.International journal of pharmaceutics: X · 2022Article
- Cell Cycle Stage Classification Using Phase Imaging with Computational Specificity.ACS photonics · 2022Article
- SPECHT: Self-tuning Plausibility based object detection Enables quantification of Conflict in Heterogeneous multi-scale microscopy.PloS one · 2022Article
- In Situ Classification of Cell Types in Human Kidney Tissue Using 3D Nuclear Staining.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2021Article
- Learning deep features for dead and living breast cancer cell classification without staining.Scientific reports · 2021Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Across the cell cycle, the subcellular organization undergoes major spatiotemporal changes that could in principle contain biological features that could potentially represent cell cycle phase. We applied convolutional neural network-based classifiers to extract such putative features from the fluorescence microscope images of cells stained for the nucleus, the Golgi apparatus, and the microtubule cytoskeleton. We demonstrate that cell images can be robustly classified according to G1/S and G2 cell cycle phases without the need for specific cell cycle markers. Grad-CAM analysis of the classification models enabled us to extract several pairs of quantitative parameters of specific subcellular features as good classifiers for the cell cycle phase. These results collectively demonstrate that machine learning-based image processing is useful to extract biological features underlying cellular phenomena of interest in an unbiased and data-driven manner.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.