000			02928nab|a22003737a|4500
001			64838
003			MX-TxCIM
005			20240919021232.0
008			202112s2021||||sz |||p|op||||00||0|eng|d
020			_a1664-8021
024	8		_ahttps://doi.org/10.3389/fgene.2021.798840
040			_aMX-TxCIM
041			_aeng
100	1		_aMontesinos-Lopez, O.A. _92700 _8I1706800 _gGenetic Resources Program
245	1	2	_aA new deep learning calibration method enhances genome-based prediction of continuous crop traits
260			_aSwitzerland : _bFrontiers, _c2021.
500			_aPeer review
500			_aOpen Access
520			_aGenomic selection (GS) has the potential to revolutionize predictive plant breeding. A reference population is phenotyped and genotyped to train a statistical model that is used to perform genome-enabled predictions of new individuals that were only genotyped. In this vein, deep neural networks, are a type of machine learning model and have been widely adopted for use in GS studies, as they are not parametric methods, making them more adept at capturing nonlinear patterns. However, the training process for deep neural networks is very challenging due to the numerous hyper-parameters that need to be tuned, especially when imperfect tuning can result in biased predictions. In this paper we propose a simple method for calibrating (adjusting) the prediction of continuous response variables resulting from deep learning applications. We evaluated the proposed deep learning calibration method (DL_M2) using four crop breeding data sets and its performance was compared with the standard deep learning method (DL_M1), as well as the standard genomic Best Linear Unbiased Predictor (GBLUP). While the GBLUP was the most accurate model overall, the proposed deep learning calibration method (DL_M2) helped increase the genome-enabled prediction performance in all data sets when compared with the traditional DL method (DL_M1). Taken together, we provide evidence for extending the use of the proposed calibration method to evaluate its potential and consistency for predicting performance in the context of GS applied to plant breeding.
546			_aText in English
650		7	_aMarker-assisted selection _2AGROVOC _910737
650		7	_aGenomics _2AGROVOC _91132
650		7	_aMachine learning _2AGROVOC _911127
650		7	_aPlant breeding _gAGROVOC _2 _91203
700	1		_aMontesinos-Lopez, A. _92702
700	1		_aMosqueda-Gonzalez, B.A. _919441
700	1		_aBentley, A.R. _8001712492 _gFormerly Global Wheat Program _99599
700	1		_aLillemo, M. _91659
700	1		_aVarshney, R.K. _95901
700	1		_aCrossa, J. _gGenetic Resources Program _8CCJL01 _959
773	0		_tFrontiers in Genetics _gv. 12, art. 798840 _dSwitzerland : Frontiers, 2021. _x1664-8021 _w58093
856	4		_yOpen Access through DSpace _uhttps://hdl.handle.net/10883/21810
942			_cJA _n0 _2ddc
999			_c64838 _d64830