Accurate performance prediction based on proprietary machine learning.

Plants in hands

Performance Prediction with xSeedScore

Reliable performance predictions are saving our breeders time that they can convert into a head start with time-to-market. Integrating predictions for locations and different climate conditions faster lets us prepare for changing climate conditions today.

Benefit from our machine learning-based regularized kernel methods to predict phenotypes from genome-wide markers. These methods model heterosis and genetic gain. We store the trained predictors to reproducibly analyze next season’s data to make results directly comparable.


  • Predict performance: Learn how your hybrid cross will perform before it is field-tested.
  • High throughput: Genotype millions of genetic markers in thousands of plant lines.
  • Fast improvement: Receive machine learning-based breeding values and phenotypes within 48 hours and improve the model with each cycle.

Client project: Hybrid Corn Breeding Program


We support a corn breeder with predicting hybrid performance of double-haploid crosses at high accuracy. Thanks to a representative training population, we obtained a highly accurate model that accelerates this program by 6 years time-to-market.

More Information

xSeedScore Information

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xSeedScore Information (971.1 KiB)

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 887865


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Tübingen, Germany

Computomics is based in the university town of Tübingen, situated in the Southwest of Germany, from where we serve clients all over the world.

We also have a presence in Madison, Wisconsin and in the Washington, DC area.

Computomics GmbH
Eisenbahnstr. 1
72072 Tübingen

Phone +49 7071 568 3995