000			03087nab|a22004457a|4500
001			67351
003			MX-TxCIM
005			20241126095823.0
008			20243s2024||||mx |||p|op||||00||0|eng|d
022			_a2073-4425 (Online)
024	8		_ahttps://doi.org/10.3390/genes15030286
040			_aMX-TxCIM
041			_aeng
100	1		_aMontesinos-Lopez, O.A. _8I1706800 _92700 _gGenetic Resources Program
245	1	0	_aData augmentation enhances plant-genomic-enabled predictions
260			_bMDPI, _c2024. _aBasel (Switzerland) :
500			_aPeer review
500			_aOpen Access
520			_aGenomic 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.
546			_aText in English
591			_aMontesinos-Lopez, O.A. : No CIMMYT Affiliation
650		7	_aMarker-assisted selection _2AGROVOC _910737
650		7	_aPlant breeding _2AGROVOC _91203
650		7	_aData _2AGROVOC _99002
700	1		_aSolis-Camacho, M.A. _933472
700	1		_aCrespo-Herrera, L.A. _8I1706538 _92608 _gGlobal Wheat Program
700	1		_aSaint Pierre, C. _8INT2731 _9855 _gGlobal Wheat Program
700	1		_aHuerta Prado, G.I. _933473
700	1		_aRamos-Pulido, S. _931496
700	0		_aKhalid Al-Nowibet _933474
700	1		_aFritsche-Neto, R. _96507
700	1		_aGerard, G.S. _81713398 _911490 _gGlobal Wheat Program
700	1		_aMontesinos-Lopez, A. _92702
700	1		_aCrossa, J. _gGenetic Resources Program _8CCJL01 _959
773	0		_tGenes _gv. 15, no. 3, art. 286 _dBasel (Switzerland) : MDPI, 2024 _x2073-4425
856	4		_yOpen Access through DSpace _uhttps://hdl.handle.net/10883/23127
942			_cJA _n0 _2ddc
999			_c67351 _d67343