Accurate performance prediction based on proprietary machine learning.


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.

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Tübingen, Germany

Computomics is based in the university town of Tübingen, situated in the Southwest of Germany, but serves clients all over the world. We also have offices in Davis, California and Madison, Wisconsin.

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Computomics GmbH
Christophstr. 32
72072 Tübingen

Phone: +49 7071 568 3995