ArticleNature methods2023
Uncovering developmental time and tempo using deep learning.
Article in Nature methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- A quantitative coordinate system for developmental dynamics.bioRxiv : the preprint server for biology · 2026Article
- MultiCell: geometric learning in multicellular development.Nature methods · 2026Article
- Variable deep learning training horizons reveal the temporal complexity of biological systems.microPublication biology · 2026Article
- DynamicAtlas: a morphodynamic atlas for Drosophila development.Nature methods · 2026Article
- Temporal Analysis of Embryonic Epidermal Morphogenesis inInternational journal of molecular sciences · 2025Article
- Deep manifold learning reveals hidden developmental dynamics of a human embryo model.Science advances · 2025Article
- Deep learning-based high-resolution time inference for deciphering dynamic gene regulation from fixed embryos.Nature communications · 2025Article
- Deep learning to assess erythritol in zebrafish development, circadian rhythm, and cardiovascular disease risk.NPJ science of food · 2025Article
- Zebrafish as a tool for autism research: unraveling the roles of Shank3, Cntnap2, Neuroligin3, and Arid1b in synaptic and behavioral abnormalities.Neurogenetics · 2025Review
- Morphometric-Assisted Prediction of Developmental Toxicity Using Stem Cell-Based Embryo Models in Microwells.Advanced healthcare materials · 2025Article
- Zebrafish identification with deep CNN and ViT architectures using a rolling training window.Scientific reports · 2025Article
- Keratinocyte-driven dermal collagen formation in the axolotl skin.Nature communications · 2025Article
- Machine learning approaches for image classification in developmental biology and clinical embryology.Development (Cambridge, England) · 2025Review
- Deep phenotypic profiling of neuroactive drugs in larval zebrafish.Nature communications · 2024Article
- Article
- Creatine and L-carnitine attenuate muscular laminopathy in the LMNA mutation transgenic zebrafish.Scientific reports · 2024Article
- Dev-ResNet: automated developmental event detection using deep learning.The Journal of experimental biology · 2024Article
- Method of the Year 2023: methods for modeling development.Nature methods · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
During animal development, embryos undergo complex morphological changes over time. Differences in developmental tempo between species are emerging as principal drivers of evolutionary novelty, but accurate description of these processes is very challenging. To address this challenge, we present here an automated and unbiased deep learning approach to analyze the similarity between embryos of different timepoints. Calculation of similarities across stages resulted in complex phenotypic fingerprints, which carry characteristic information about developmental time and tempo. Using this approach, we were able to accurately stage embryos, quantitatively determine temperature-dependent developmental tempo, detect naturally occurring and induced changes in the developmental progression of individual embryos, and derive staging atlases for several species de novo in an unsupervised manner. Our approach allows us to quantify developmental time and tempo objectively and provides a standardized way to analyze early embryogenesis.
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