Genes, Vol. 15, Pages 286: Data Augmentation Enhances Plant-Genomic-Enabled Predictions

Genes, Vol. 15, Pages 286: Data Augmentation Enhances Plant-Genomic-Enabled Predictions Genes doi: 10.3390/genes15030286 Authors: Osval A. Montesinos-López Mario Alberto Solis-Camacho Leonardo Crespo-Herrera Carolina Saint Pierre Gloria Isabel Huerta Prado Sofia Ramos-Pulido Khalid Al-Nowibet Roberto Fritsche-Neto Guillermo Gerard Abelardo Montesinos-López José Crossa 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 top lines in the 14 datasets under study. On average, across datasets and traits, the gain in prediction performance of the DA approach regarding the Conventional method in the top 20% of lines in the testing set was 108.4% in terms of the NRMSE and 107.4% in terms of the MAAPE, but a worse perform...
Source: Genes - Category: Genetics & Stem Cells Authors: Tags: Article Source Type: research