ArticleNature methods2023
EmbryoNet: using deep learning to link embryonic phenotypes to signaling pathways.
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 27 papers.
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Who cites it
27 citing papers in PubMed.
- Article
- From pixels to patterns: the AI revolution in stem cell-derived models.Nature methods · 2026Review
- Zebrafish swimming towards cures: a scalable NAM platform for drug discovery.Drug discovery today · 2026Review
- Explainable AI consensus for grading human blastocysts and blastoids.Reproduction & fertility · 2026Article
- A quantitative coordinate system for developmental dynamics.bioRxiv : the preprint server for biology · 2026Article
- Dual-channel graph learning reveals similarity and complementarity in protein-protein interaction networks.PLoS computational biology · 2026Article
- Decoding the modulatory role of long non-coding RNAs in embryogenesis: a comprehensive review.Molecular biology reports · 2026Review
- deepBlastoid: a deep learning model for automated and efficient evaluation of human blastoids.Life medicine · 2025Article
- Combining deep learning and microfluidics for fast and noninvasive sorting of zebrafish embryo.Scientific reports · 2025Article
- Deep learning models link local cellular features with whole-animal growth dynamics in zebrafish.Life science alliance · 2025Article
- Deep learning-based high-resolution time inference for deciphering dynamic gene regulation from fixed embryos.Nature communications · 2025Article
- Morphometric-Assisted Prediction of Developmental Toxicity Using Stem Cell-Based Embryo Models in Microwells.Advanced healthcare materials · 2025Article
- Developmental toxicity: artificial intelligence-powered assessments.Trends in pharmacological sciences · 2025Review
- Label-free evaluation of mouse embryo quality using time-lapse bright field and optical coherence microscopy.Communications biology · 2025Article
- AI-based approach to dissect the variability of mouse stem cell-derived embryo models.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
- Dev-ResNet: automated developmental event detection using deep learning.The Journal of experimental biology · 2024Article
- Article
- Deep learning reduces data requirements and allows real-time measurements in imaging FCS.Biophysical journal · 2024Article
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Authors and funding
12 authors.
Funding
Abstract
Evolutionarily conserved signaling pathways are essential for early embryogenesis, and reducing or abolishing their activity leads to characteristic developmental defects. Classification of phenotypic defects can identify the underlying signaling mechanisms, but this requires expert knowledge and the classification schemes have not been standardized. Here we use a machine learning approach for automated phenotyping to train a deep convolutional neural network, EmbryoNet, to accurately identify zebrafish signaling mutants in an unbiased manner. Combined with a model of time-dependent developmental trajectories, this approach identifies and classifies with high precision phenotypic defects caused by loss of function of the seven major signaling pathways relevant for vertebrate development. Our classification algorithms have wide applications in developmental biology and robustly identify signaling defects in evolutionarily distant species. Furthermore, using automated phenotyping in high-throughput drug screens, we show that EmbryoNet can resolve the mechanism of action of pharmaceutical substances. As part of this work, we freely provide more than 2 million images that were used to train and test EmbryoNet.
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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 Socratic graph.