000			03156nab|a22004697a|4500
001			67683
003			MX-TxCIM
005			20240919021234.0
008			20241s2024||||mx |||p|op||||00||0|eng|d
022			_a1664-462X (Online)
024	8		_ahttps://doi.org/10.3389/fpls.2024.1349569
040			_aMX-TxCIM
041			_aeng
100	1		_aMontesinos-Lopez, O.A. _gGenetic Resources Program _8I1706800 _92700
245	1	0	_aFeature engineering of environmental covariates improves plant genomic-enabled prediction
260			_bFrontiers Media S.A., _c2024. _aSwitzerland :
500			_aPeer review
500			_aOpen Access
520			_aIntroduction: Because Genomic selection (GS) is a predictive methodology, it needs to guarantee high-prediction accuracies for practical implementations. However, since many factors affect the prediction performance of this methodology, its practical implementation still needs to be improved in many breeding programs. For this reason, many strategies have been explored to improve the prediction performance of this methodology. Methods: When environmental covariates are incorporated as inputs in the genomic prediction models, this information only sometimes helps increase prediction performance. For this reason, this investigation explores the use of feature engineering on the environmental covariates to enhance the prediction performance of genomic prediction models. Results and discussion: We found that across data sets, feature engineering helps reduce prediction error regarding only the inclusion of the environmental covariates without feature engineering by 761.625% across predictors. These results are very promising regarding the potential of feature engineering to enhance prediction accuracy. However, since a significant gain in prediction accuracy was observed in only some data sets, further research is required to guarantee a robust feature engineering strategy to incorporate the environmental covariates.
546			_aText in English
591			_aMontesinos-Lopez, O.A. : No CIMMYT Affiliation
650		7	_aEngineering _2AGROVOC _923223
650		7	_aSelection _2AGROVOC _94749
650		7	_aMarker-assisted selection _2AGROVOC _910737
650		7	_aPlant breeding _gAGROVOC _2 _91203
700	1		_aCrespo-Herrera, L.A. _gGlobal Wheat Program _8I1706538 _92608
700	1		_aSaint Pierre, C. _gGlobal Wheat Program _8INT2731 _9855
700	1		_aCano-Paez, B. _929337
700	1		_aHuerta Prado, G.I. _933473
700	1		_aMosqueda-Gonzalez, B.A. _919441
700	1		_aRamos-Pulido, S. _931496
700	1		_aGerard, G.S. _81713398 _gGlobal Wheat Program _911490
700	0		_aKhalid Alnowibet _934425
700	1		_aFritsche-Neto, R. _96507
700	1		_aMontesinos-Lopez, A. _92702
700	1		_aCrossa, J. _gGenetic Resources Program _8CCJL01 _959
773	0		_tFrontiers in Plant Science _gv. 15, art. 1349569 _dSwitzerland : Frontiers Media S.A., 2024. _w56875 _x1664-462X
856	4		_yOpen Access through DSpace _uhttps://hdl.handle.net/10883/34616
942			_cJA _n0 _2ddc
999			_c67683 _d67675