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.
Computomics Expert on Genomic Prediction
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 887865
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.
Phone +49 7071 568 3995