Applying Machine Learning To Maize Traits Prediction
This work addresses trait prediction for maize breeding, but it appears incremental as it applies existing machine learning methods to a new, larger dataset.
The authors tackled the problem of predicting maize traits using the largest maize SNP dataset to date, developing linear and non-linear models that incorporate hybrid relationships and other effects, with a specially designed model proving efficient and robust in prediction.
Heterosis is the improved or increased function of any biological quality in a hybrid offspring. We have studied yet the largest maize SNP dataset for traits prediction. We develop linear and non-linear models which consider relationships between different hybrids as well as other effect. Specially designed model proved to be efficient and robust in prediction maize's traits.