Automated decision making in biotech

Short description:

  • Inbiose creates specialty carbohydrate, glycans that unlock innovations in the areas of nutrition, health, life sciences and beyond
  • The wet labs of Inbiose generate large amount of data every day

Work performed:

  • Advising Inbiose on how to set up decision support systems
  • Analysis of biological data with machine learning
  • Optimizing database structure
  • Machine learning pipelines for decision making

Added value:

Machine learning allows inbiose to make data-driven decisions on the most promising variable space, combining both discovery and optimization paths. This enables a focus on better scientific results with far fewer investments in less promising experiments.

Context:

Nature uses complex carbohydrates for a wide range of biological functions and has a wide variety of complex systems to build these carbohydrates.

For example, it’s a scientific fact that breastmilk is better for an infant than cow milk or other replacement products. However, it’s not always possible for the mother to provide their child with breastmilk. But what makes breastmilk so unique, why is it better for the immune system? The key in this lies in specialty glycans, in particular Human Milk Oligosaccharides (HMOs).

These specialty glycans are known to influence health, either directly or indirectly through modulation of the microbiome.

Inbiose is a leading B2B Biotech Company with a focus on the development of these special glycans. What was started in 2013 as a spinoff from the University of Ghent, they are now the biggest private research group in the world of specialty glycans.

They continuously improve their technology platform based on the latest advancements in biotech, bioinformatics, health sciences and now with machine learning.

Inbiose explores the possibilities of machine learning as the driver of a currently human-driven optimization process. With the help of ML2Grow, they strive to make these specialty glycans for mainstream uses, minimizing time to market, to the benefit of all.

 

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