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Computomics prevails in most competitive EIC Accelerator, receives multi-million Euros in funding

Computomics prevails in EIC Accelerator

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Computomics prevails in most competitive EIC Accelerator, receives several million Euros in funding

  • Computomics AI predicts performance of crops and novel plant crosses for any location, taking future climate change into account
  • Plant breeders receive more choices for the launch of new varieties
  • Investment highlights the importance of developing solutions for the global agricultural sector

Tübingen, Germany, 23 June, 2022 –  The EIC Accelerator is one of the most competitive and most renowned funding programs worldwide, supporting selected Small and Medium-sized Enterprises (SMEs) to scale up their game-changing innovations. More than 1,000 start-ups and SMEs submitted a full application for funding in March 2022, of which 266 were interviewed by juries of experienced investors and entrepreneurs. Finally, the European Commission selected 74 innovative companies to receive funding and support. “By being selected, Computomics has clearly demonstrated to be part of the ‘Champions League’ of European start-ups,” says Dr. Sebastian J. Schultheiss, co-founder and Managing Director of Computomics.

Plant breeding secures the supply and food quality for an increasing population. Growing healthy plants adapted to a changing climate in future harsher environmental conditions requires innovation. Plant breeders should be in a position where they can make use of the full potential of the available genetic plant material, identify new favorable traits, and speed up their breeding process. Current methods, however, have low success rates in adapting to new climate conditions, missing superior lines because they recommend crosses that worked well in the past.

⨉SeedScore® is a unique machine learning-based technology that uses bioinformatics data to identify the best-performing plants for a range of future climate conditions. It supports traditional plant breeding methods and integrates additional variables such as plant genetics, plant traits (characteristics), the cultivation environment, the field management used and information on climate, weather, and soil, without the need for genetic modification. The result is a performance prediction delivering 10 times more candidates for commercial evaluation, speeding up the plant breeding process. On top of that, it allows the plant breeder to select crops that will be suitable for the weather patterns anticipated by climate change, and to select crosses for maximum performance — years ahead of typical breeding schedules. It enables decision making based on accurate data.

“In short, ⨉SeedScore® is the future of innovative plant breeding. We are proud to be recognized by the European Commission for this game-changing technology and, within the Accelerator, to be able to further advance it,” says Dr. Schultheiss.

The multi-million Euro funding by EIC for Computomics highlights the importance of developing solutions for the global agricultural sector and emphasizes the trust in our technology. Computomics will use the investment to further develop ⨉SeedScore®.

 

About Computomics

Computomics offers a proprietary machine learning technology platform that applies AI to genetics, phenotype, microbiome, and environmental datasets. Computomics is a team of world-leading experts in machine learning, plant research and bioinformatics, who use data to unlock the diversity of biological life. In over 180 projects, Computomics enabled customers to make data-driven decisions and thereby accelerate sustainable agricultural development that can feed the world. Computomics’ interpretable machine learning technology enables rapid understanding of genomic data for plant breeding, agricultural biotech, and microbiome researchers. To learn more about Computomics, please visit computomics.com or follow Computomics on Twitter at twitter.com/Computomics.

 

Press Contact:
Computomics GmbH
Simone Betz
Phone: +49 7071 5683995
Email: simone.betz@computomics.com

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