ArticleeLife2024
Deep learning for rapid analysis of cell divisions in vivo during epithelial morphogenesis and repair.
Article in eLife, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- α-Ketoglutarate accelerates cutaneous wound healing through modulating the epithelial-fibroblast niche.JCI insight · 2026Article
- Single-cell morphodynamics predict cell fate decisions during mucociliary epithelial differentiation.Molecular systems biology · 2026Article
- EpiCure (Epithelial Curation): a versatile and handy tool for curation of epithelial segmentation.bioRxiv : the preprint server for biology · 2026Article
- Artificial intelligence in mitotic checkpoint modeling: transforming our understanding of cellular division through machine learning and predictive biology.Briefings in bioinformatics · 2026Review
- Single-cell morphodynamics predict cell fate decisions during mucociliary epithelial differentiation.bioRxiv : the preprint server for biology · 2025Article
- Exploring machine learning strategies for single-cell transcriptomic analysis in wound healing.Burns & trauma · 2025Review
- Novel imaging and biophysical approaches to study tissue hydraulics in mammalian folliculogenesis.Biophysical reviews · 2024Review
- Deep learning for rapid analysis of cell divisions in vivo during epithelial morphogenesis and repair.eLife · 2024Article
- Deep learning reveals a damage signalling hierarchy that coordinates different cell behaviours driving wound re-epithelialisation.Development (Cambridge, England) · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Cell division is fundamental to all healthy tissue growth, as well as being rate-limiting in the tissue repair response to wounding and during cancer progression. However, the role that cell divisions play in tissue growth is a collective one, requiring the integration of many individual cell division events. It is particularly difficult to accurately detect and quantify multiple features of large numbers of cell divisions (including their spatio-temporal synchronicity and orientation) over extended periods of time. It would thus be advantageous to perform such analyses in an automated fashion, which can naturally be enabled using deep learning. Hence, we develop a pipeline of deep learning models that accurately identify dividing cells in time-lapse movies of epithelial tissues in vivo. Our pipeline also determines their axis of division orientation, as well as their shape changes before and after division. This strategy enables us to analyse the dynamic profile of cell divisions within the
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What OpenQuestion holds
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.