EmbryoNet · Part of Morphomics
Deep-learning analysis of zebrafish embryos and organoids
for quantitative developmental phenotyping
EmbryoNet is the phenotype classifier within the Morphomics platform, which combines automated imaging with AI-powered analysis.
Morphomics Technologies uses deep learning to support toxicology studies and drug screening in organoids and embryos. Our customized AI solutions are designed to meet the unique needs of our clients, providing rapid, accurate, and comprehensive insights into the effects of compounds on biological development.
EmbryoNet has successfully identified novel compounds and elucidated their mechanisms, contributing valuable insights into therapeutic development. Its precise classification capabilities have also opened new pathways in genetic research, offering solutions to long-standing questions in developmental biology. By facilitating a deeper understanding of developmental processes, EmbryoNet holds the promise for groundbreaking advancements in medicine and biotechnology.
Our next chapter
Morphomics brings together MorphoScan automated imaging, EmbryoNet phenotype classification, MorphoNet tissue segmentation and TwinNet developmental staging. Explore the platform, try the web app and follow our practical tutorials at morphomics.de.
These services are now offered through Morphomics Technologies.
Utilize our AI-driven platform, EmbryoNet, to analyze and classify embryonic and organoid phenotypes with unmatched accuracy and speed. This service enables detailed understanding of genetic and environmental impacts on development, facilitating groundbreaking discoveries in biology and medicine.
We offer services for a variety of species to ensure that our clients can receive relevant insights regardless of their model organism.
For clients with specific research needs, we develop tailor-made AI models. Our team works closely with you to understand your objectives and create a solution that seamlessly integrates with your workflows, enhancing your research capabilities.
Our technology excels in identifying the developmental impacts of pharmaceutical substances. By automating the drug screening process, we significantly reduce the time and cost associated with traditional methods, allowing for the efficient discovery of novel therapeutics and the elucidation of their mechanisms of action.
EmbryoNet provides detailed toxicological profiles of compounds, leveraging deep learning to predict adverse effects with high reliability. This service supports safer drug development and chemical use by identifying potential teratogenic risks early in the product lifecycle.
We offer screening services with zebrafish embryos and larvae for your compounds at the concentrations of your choice.
At Morphomics Technologies, we are committed to advancing the field of life sciences through the power of AI. Contact us today to learn how our services can transform your research and development endeavors.
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.
Read moreToulany 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.
Read moreMüller P et al. (2023). AI identifies developmental defects and drug mechanisms in embryos. Nature Methods, 20:793–794.
Read moreIn vitro embryo models supported by methods development in adjacent fields have revolutionized our understanding of embryogenesis.
Read moreNew funding
Morphomics Technologies has been awarded an EXIST Business Start-up Grant. The funding supports the development of MorphoScan automated imaging and the Morphomics platform for AI-powered phenotypic analysis in research, drug discovery and toxicology.
About the EXIST programme (opens in a new tab)Earlier funding
23 January 2025 — EmbryoNet AI Technologies proudly announces receiving the prestigious ERC Proof of Concept grant. This funding will accelerate the commercialization of our cutting-edge AI solutions for drug discovery and toxicology. We extend our gratitude to the European Research Council for supporting our mission.
Why choose Morphomics Technologies?
We have supported clients across Germany, Poland, Italy, and Finland.
At the forefront of AI and biological research, our solutions offer a new dimension of insight into developmental biology.
Our deep learning models provide rapid and precise analysis, significantly outperforming traditional methods.
We understand that each research project is unique. Our customized approach ensures that our services meet your specific needs.
Our team of experts in AI, biology, and pharmacology is dedicated to supporting your projects from inception to completion.
Contact the Morphomics Technologies team.
Feel free to contact us for a quote. We can help you develop AI solutions tailored to your problem. Together, we can unlock new potentials in developmental biology and pharmacology.
Morphomics Technologies
hello@morphomics.de
patrick.mueller@embryonet.ai
+49(0)7531 885544
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is a type of machine learning that uses neural networks with many layers to automatically learn and extract complex patterns from large amounts of data, enabling advanced tasks like image recognition, language processing, and more.
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are miniature, simplified versions of organs grown in vitro from stem cells, used for studying organ development, disease modeling, and drug testing.
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are early stages of development in multicellular organisms, formed after fertilization and before becoming a fetus, where the basic structures and organs begin to develop.
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refers to artificial intelligence systems specifically tailored to meet unique needs and requirements of a particular user or organization, enhancing performance and relevance for specific tasks or applications.
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are low molecular weight organic chemicals that can easily enter cells, often used as drugs to modulate biological processes and treat diseases.