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022			_a1664-462X (Online)
024	8		_ahttps://doi.org/10.3389/fpls.2024.1324090
040			_aMX-TxCIM
041			_aeng
100	1		_aMontesinos-Lopez, A. _92702
245	1	0	_aDeep learning methods improve genomic prediction of wheat breeding
260			_bFrontiers Media S.A., _c2024. _aSwitzerland :
500			_aPeer review
500			_aOpen Access
520			_aIn the field of plant breeding, various machine learning models have been developed and studied to evaluate the genomic prediction (GP) accuracy of unseen phenotypes. Deep learning has shown promise. However, most studies on deep learning in plant breeding have been limited to small datasets, and only a few have explored its application in moderate-sized datasets. In this study, we aimed to address this limitation by utilizing a moderately large dataset. We examined the performance of a deep learning (DL) model and compared it with the widely used and powerful best linear unbiased prediction (GBLUP) model. The goal was to assess the GP accuracy in the context of a five-fold cross-validation strategy and when predicting complete environments using the DL model. The results revealed the DL model outperformed the GBLUP model in terms of GP accuracy for two out of the five included traits in the five-fold cross-validation strategy, with similar results in the other traits. This indicates the superiority of the DL model in predicting these specific traits. Furthermore, when predicting complete environments using the leave-one-environment-out (LOEO) approach, the DL model demonstrated competitive performance. It is worth noting that the DL model employed in this study extends a previously proposed multi-modal DL model, which had been primarily applied to image data but with small datasets. By utilizing a moderately large dataset, we were able to evaluate the performance and potential of the DL model in a context with more information and challenging scenario in plant breeding.
546			_aText in English
591			_aMontesinos-Lopez, O.A. : No CIMMYT Affiliation
650		7	_aGenomics _2AGROVOC _91132
650		7	_aForecasting _2AGROVOC _92701
650		7	_aMachine learning _2AGROVOC _911127
650		7	_aLearning _2AGROVOC _911157
650		7	_aWheat _2AGROVOC _91310
650		7	_aBreeding _2AGROVOC _91029
700	1		_aCrespo-Herrera, L.A. _8I1706538 _92608 _gGlobal Wheat Program
700	1		_aDreisigacker, S. _8INT2692 _9851 _gGlobal Wheat Program
700	1		_aGerard, G.S. _81713398 _911490 _gGlobal Wheat Program
700	1		_aVitale, P. _8001713327 _931497 _gGlobal Wheat Program
700	1		_aSaint Pierre, C. _8INT2731 _9855 _gGlobal Wheat Program
700	1		_aVelu, G. _8INT2983 _9880 _gGlobal Wheat Program
700	1		_aTarekegn, Z.T. _8001713397 _931150 _gGlobal Wheat Program
700	1		_aChavira-Flores, M. _929566
700	1		_aPerez-Rodriguez, P. _92703
700	1		_aRamos-Pulido, S. _931496
700	1		_aLillemo, M. _91659
700	1		_aHuihui Li _8CLIH01 _9764 _gGenetic Resources Program
700	1		_aMontesinos-Lopez, O.A. _8I1706800 _92700 _gGenetic Resources Program
700	1		_aCrossa, J. _gGenetic Resources Program _8CCJL01 _959
773	0		_tFrontiers in Plant Science _gv. 15, art. 1324090 _dSwitzerland : Frontiers Media S.A., 2024. _x1664-462X
856	4		_yOpen Access through DSpace _uhttps://hdl.handle.net/10883/23118
942			_cJA _n0 _2ddc
999			_c67343 _d67335