Data augmentation enhances plant-genomic-enabled predictions
| cg.authorship.types | CGIAR and advanced research institute | en_US |
| cg.authorship.types | CGIAR and developing country institute | en_US |
| cg.authorship.types | CGIAR single centre | en_US |
| cg.contributor.affiliation | Universidad de Colima | en_US |
| cg.contributor.affiliation | International Maize and Wheat Improvement Center | en_US |
| cg.contributor.affiliation | Universidad de Guadalajara | en_US |
| cg.contributor.affiliation | King Saud University | en_US |
| cg.contributor.affiliation | Louisiana State University | en_US |
| cg.contributor.affiliation | Colegio de Postgraduados, Mexico | en_US |
| cg.contributor.donor | Bill & Melinda Gates Foundation | en_US |
| cg.contributor.donor | United States Agency for International Development | en_US |
| cg.contributor.donor | Norwegian Research Council | en_US |
| cg.contributor.donor | King Saud University | en_US |
| cg.contributor.donor | CGIAR Trust Fund | en_US |
| cg.contributor.initiative | Accelerated Breeding | en_US |
| cg.creator.identifier | Osval A. Montesinos-López: 0000-0002-3973-6547 | en_US |
| cg.creator.identifier | Mario Alberto Solís Camacho: 0009-0004-7858-5173 | en_US |
| cg.creator.identifier | Leonardo Abdiel Crespo Herrera: 0000-0003-0506-4700 | en_US |
| cg.creator.identifier | Carolina Saint Pierre: 0000-0003-1291-7468 | en_US |
| cg.creator.identifier | Sofía Ramos-Pulido: 0000-0003-0101-4511 | en_US |
| cg.creator.identifier | KHALID ALNOWIBET: 0000-0001-5760-0216 | en_US |
| cg.creator.identifier | Roberto Fritsche-Neto: 0000-0003-4310-0047 | en_US |
| cg.creator.identifier | Guillermo Gerard: 0000-0002-9112-3588 | en_US |
| cg.creator.identifier | Jose Crossa: 0000-0001-9429-5855 | en_US |
| cg.howPublished | Formally Published | en_US |
| cg.identifier.doi | https://doi.org/10.3390/genes15030286 | en_US |
| cg.identifier.url | https://hdl.handle.net/10883/23127 | en_US |
| cg.isijournal | ISI Journal | en_US |
| cg.issn | 2073-4425 | en_US |
| cg.issue | 3 | en_US |
| cg.journal | Genes | en_US |
| cg.reviewStatus | Peer Review | en_US |
| cg.subject.actionArea | Genetic Innovation | en_US |
| cg.subject.impactArea | Nutrition, health and food security | en_US |
| cg.volume | 15 | en_US |
| dc.contributor.author | Montesinos-Lopez, Osval A. | en_US |
| dc.contributor.author | Solis-Camacho, Mario Alberto | en_US |
| dc.contributor.author | Crespo Herrera, Leonardo A. | en_US |
| dc.contributor.author | Saint Pierre, Carolina | en_US |
| dc.contributor.author | Huerta Prado, Gloria Isabel | en_US |
| dc.contributor.author | Ramos-Pulido, Sofia | en_US |
| dc.contributor.author | Al-Nowibet, Khalid | en_US |
| dc.contributor.author | Fritsche-Neto, Roberto | en_US |
| dc.contributor.author | Gerard, Guillermo S. | en_US |
| dc.contributor.author | Montesinos-Lopez, Abelardo | en_US |
| dc.contributor.author | Crossa, José | en_US |
| dc.date.accessioned | 2024-11-15T14:51:00Z | en_US |
| dc.date.available | 2024-11-15T14:51:00Z | en_US |
| dc.identifier.uri | https://hdl.handle.net/10568/159826 | en_US |
| dc.title | Data augmentation enhances plant-genomic-enabled predictions | en_US |
| dcterms.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 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. | en_US |
| dcterms.accessRights | Open Access | en_US |
| dcterms.audience | Academics | en_US |
| dcterms.available | 2024-02-21 | en_US |
| dcterms.bibliographicCitation | Montesinos-López, O. A., Solis-Camacho, M. A., Crespo-Herrera, L., Saint Pierre, C., Huerta Prado, G. I., Ramos-Pulido, S., Al-Nowibet, K., Fritsche-Neto, R., Gerard, G. S., Montesinos-López, A., & Crossa, J. (2024). Data augmentation enhances plant-genomic-enabled predictions. Genes, 15(3), 286. https://doi.org/10.3390/genes15030286 | en_US |
| dcterms.extent | 286 | en_US |
| dcterms.issued | 2024-03 | en_US |
| dcterms.language | en | en_US |
| dcterms.license | CC-BY-4.0 | en_US |
| dcterms.publisher | MDPI | en_US |
| dcterms.subject | marker-assisted selection | en_US |
| dcterms.subject | plant breeding | en_US |
| dcterms.subject | data | en_US |
| dcterms.subject | genomes | en_US |
| dcterms.type | Journal Article | en_US |