The original description of the technology underlying EmbryoNet
Čapek D, Safroshkin M, Morales-Navarrete H, Toulany N, Arutyunuv G, Kurzbach A, Bihler J, Hagauer J, Kick S, Jones F, Jordan B, Müller P (2023). EmbryoNet: Using deep learning to link embryonic phenotypes to signaling pathways. Nature Methods, 20:815–823.
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Extension of AI technologies for developmental trajectories
Toulany N, Morales-Navarrete H, Čapek D, Grathwohl J, Ünalan M, Müller P (2023). Uncovering developmental time and tempo using deep learning. Nature Methods, 20:2000–2010.
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AI identifies developmental defects and drug mechanisms in embryos
Müller P et al. (2023). AI identifies developmental defects and drug mechanisms in embryos. Nature Methods, 20:793–794.
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EmbryoNet is part of the Nature Methods of the Year 2023 feature
In vitro embryo models supported by methods development in adjacent fields have revolutionized our understanding of embryogenesis.
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