TY  - JA
AU  - Montesinos-Lopez,O.A.
AU  - Solis-Camacho,M.A.
AU  - Crespo-Herrera,L.A.
AU  - Saint Pierre,C.
AU  - Huerta Prado,G.I.
AU  - Ramos-Pulido,S.
AU  - Khalid Al-Nowibet
AU  - Fritsche-Neto,R.
AU  - Gerard,G.S.
AU  - Montesinos-Lopez,A.
AU  - Crossa,J.
TI  - Data augmentation enhances plant-genomic-enabled predictions
SN  - 2073-4425
PY  - 2024///
CY  - Basel (Switzerland)
PB  - MDPI
KW  - Marker-assisted selection
KW  - AGROVOC
KW  - Plant breeding
KW  - Data
N1  - Peer review; Open Access
N2  - 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 performance was observed on the whole testing set. We encourage more empirical evaluations to support our findings
UR  - https://hdl.handle.net/10883/23127
T2  - Genes
DO  - https://doi.org/10.3390/genes15030286
ER  -