Data Augmentation Enhances Plant-Genomic-Enabled Predictions.

ABSTRACT: Genomic selection (GS) is revolutionizing plant breeding. However, its practical implementation is still challenging, since there are many factors that affect its accuracy. For this reason, this research explores data augmentation with the goal of improving its accuracy. Deep neural networks with data augmentation (DA) generate synthetic data from the original training set to increase the training set and to improve the prediction performance of any statistical or machine learning algorithm. There is much empirical evidence of their success in many computer vision applications. Due to this, DA was explored in the context of GS using 14 real datasets. We found empirical evidence that DA is a powerful tool to improve the prediction accuracy, since we improved the prediction accuracy of the t

SUBMITTER: Montesinos-Lopez OA 

PROVIDER: S-EPMC10969940 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature