EMBL Enhances Microbiology Methods with Deep Learning
Customer Company Size
Large Corporate
Region
- Europe
Country
- Germany
- France
- Italy
- Spain
- United Kingdom
Product
- DeepCycle
- Neu.ro Platform
Tech Stack
- Deep Learning
- AI
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Innovation Output
- Digital Expertise
Technology Category
- Analytics & Modeling - Machine Learning
- Analytics & Modeling - Computer Vision Software
Applicable Industries
- Healthcare & Hospitals
- Life Sciences
Applicable Functions
- Product Research & Development
Use Cases
- Predictive Quality Analytics
- Computer Vision
Services
- Data Science Services
About The Customer
Founded in 1974, EMBL is Europe’s flagship laboratory for the life sciences. It is an intergovernmental organisation with more than 80 independent research groups covering the spectrum of molecular biology. EMBL operates across six sites: Heidelberg, Barcelona, Hamburg, Grenoble, Rome and EMBL-EBI Hinxton. The organization is renowned for its research in molecular biology and has a strong focus on developing new technology and methods in the field. EMBL's research is conducted by more than 1600 employees from more than 80 nations and its budget is funded by public research monies from its member states.
The Challenge
Researchers at EMBL, Europe’s flagship laboratory for the life sciences, were looking to enhance traditional microbiology methods with Deep Learning. Their goal was to reconstruct the complex biological phenomena that underpin the life cycle of cells. This was a significant challenge due to the complexity of cell life cycles and the limitations of traditional microbiology methods. EMBL operates across six sites in Europe and has more than 80 independent research groups covering the spectrum of molecular biology. The challenge was to develop a solution that could accurately model the lifecycle of cells and provide insights into complex biological processes.
The Solution
EMBL collaborated with global AI researchers, including Neuromation Chief Research Officer, Sergey Nikolenko, and Senior AI Researcher, Alexander Rakhlin, to develop DeepCycle. DeepCycle is an AI-driven technology that models the lifecycle of cells - how they grow and divide. The solution was developed on the Neu.ro Platform, a platform designed for developing and deploying AI and Machine Learning solutions. Using approximately 2.6 million microscopy images of canine kidney cells, the novel deep learning model was able to reconstruct complex biological phenomena based solely on visual data. This innovative approach has potential applications in cancer research and other areas of healthcare.
Operational Impact
Quantitative Benefit
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