Applying Machine Learning To Maize Traits Prediction

TL;DR

This paper introduces advanced machine learning models to predict maize traits using a large SNP dataset, demonstrating improved accuracy and robustness in trait prediction for hybrid maize.

Contribution

It presents novel linear and non-linear models that effectively incorporate hybrid relationships and genetic effects for maize trait prediction.

Findings

Models achieved high prediction accuracy.

Non-linear models outperformed linear ones.

The approach is robust across different maize traits.

Abstract

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.