Genomic prediction in plants: opportunities for ensemble machine learning based approaches.

ABSTRACT: Background: Many studies have demonstrated the utility of machine learning (ML) methods for genomic prediction (GP) of various plant traits, but a clear rationale for choosing ML over conventionally used, often simpler parametric methods, is still lacking. Predictive performance of GP models might depend on a plethora of factors including sample size, number of markers, population structure and genetic architecture. Methods: Here, we investigate which problem and dataset characteristics are related to good performance of ML methods for genomic prediction. We compare the predictive performance of two frequently used ensemble ML methods (Random Forest and Extreme Gradient Boosting) with parametric methods including genomic best linear unbiased prediction (GBLUP), reproducing ke

SUBMITTER: Farooq M 

PROVIDER: S-EPMC10080209 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature