ArticleDevelopment (Cambridge, England)2021
Deep learning is widely applicable to phenotyping embryonic development and disease.
Article in Development (Cambridge, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 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.
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Who cites it
19 citing papers in PubMed, 39 citations in OpenAlex.
- SIX1 branchio-oto-renal syndrome variants have different effects on embryonic craniofacial gene expression and cartilage formation.Development (Cambridge, England) · 2026Article
- Precise, predictable genome integrations by deep-learning-assisted design of microhomology-based templates.Nature biotechnology · 2026Article
- Article
- HarnessingSensors (Basel, Switzerland) · 2025Review
- Deep learning models link local cellular features with whole-animal growth dynamics in zebrafish.Life science alliance · 2025Article
- Novel Variants in VARS2 Demonstrate the Phenotypic Variability of a Rare Mitochondriopathy That Responds to Valine Supplementation.Journal of inherited metabolic disease · 2025Article
- Loss of function of the zinc finger homeobox 4 gene, ZFHX4, underlies a neurodevelopmental disorder.American journal of human genetics · 2025Article
- Machine learning approaches for image classification in developmental biology and clinical embryology.Development (Cambridge, England) · 2025Review
- Cross-Shaped Heat Tensor Network for Morphometric Analysis Using Zebrafish Larvae Feature Keypoints.Sensors (Basel, Switzerland) · 2024Article
- Modelling human genetic disorders in Xenopus tropicalis.Disease models & mechanisms · 2024Review
- Benchtop mesoSPIM: a next-generation open-source light-sheet microscope for cleared samples.Nature communications · 2024Article
- Opportunities and Challenges in Applying AI to Evolutionary Morphology.Integrative organismal biology (Oxford, England) · 2024Review
- Reflective multi-immersion microscope objectives inspired by the Schmidt telescope.Nature biotechnology · 2024Article
- Virtual tissue microstructure reconstruction across species using generative deep learning.PloS one · 2024Article
- The Benchtop mesoSPIM: a next-generation open-source light-sheet microscope for large cleared samples.bioRxiv : the preprint server for biology · 2023Article
- Uncovering developmental time and tempo using deep learning.Nature methods · 2023Article
- EmbryoNet: using deep learning to link embryonic phenotypes to signaling pathways.Nature methods · 2023Article
- High-throughput image analysis with deep learning captures heterogeneity and spatial relationships after kidney injury.Scientific reports · 2023Article
- Roles of Skeletal Muscle in Development: A Bioinformatics and Systems Biology Overview.Advances in anatomy, embryology, and cell biology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
19 authors at 6 institutions in 4 countries.
Funding
Abstract
Genome editing simplifies the generation of new animal models for congenital disorders. However, the detailed and unbiased phenotypic assessment of altered embryonic development remains a challenge. Here, we explore how deep learning (U-Net) can automate segmentation tasks in various imaging modalities, and we quantify phenotypes of altered renal, neural and craniofacial development in Xenopus embryos in comparison with normal variability. We demonstrate the utility of this approach in embryos with polycystic kidneys (pkd1 and pkd2) and craniofacial dysmorphia (six1). We highlight how in toto light-sheet microscopy facilitates accurate reconstruction of brain and craniofacial structures within X. tropicalis embryos upon dyrk1a and six1 loss of function or treatment with retinoic acid inhibitors. These tools increase the sensitivity and throughput of evaluating developmental malformations caused by chemical or genetic disruption. Furthermore, we provide a library of pre-trained networks and detailed instructions for applying deep learning to the reader's own datasets. We demonstrate the versatility, precision and scalability of deep neural network phenotyping on embryonic disease models. By combining light-sheet microscopy and deep learning, we provide a framework for higher-throughput characterization of embryonic model organisms. This article has an associated 'The people behind the papers' interview.
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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.