000			02544nab|a22003857a|4500
001			65173
003			MX-TxCIM
005			20240919021233.0
008			20223s2022||||mx |||p|op||||00||0|eng|d
022			_a1664-462X
024	8		_ahttps://doi.org/10.3389/fpls.2022.845524
040			_aMX-TxCIM
041			_aeng
100	1		_aGalli, G. _98650
245	1	1	_aAutomated Machine Learning : _bA Case Study of Genomic “Image-Based” Prediction in Maize Hybrids
260			_bFrontiers, _c2022. _aSwitzerland :
500			_aPeer review
500			_aOpen Access
520			_aMachine learning methods such as multilayer perceptrons (MLP) and Convolutional Neural Networks (CNN) have emerged as promising methods for genomic prediction (GP). In this context, we assess the performance of MLP and CNN on regression and classification tasks in a case study with maize hybrids. The genomic information was provided to the MLP as a relationship matrix and to the CNN as “genomic images.” In the regression task, the machine learning models were compared along with GBLUP. Under the classification task, MLP and CNN were compared. In this case, the traits (plant height and grain yield) were discretized in such a way to create balanced (moderate selection intensity) and unbalanced (extreme selection intensity) datasets for further evaluations. An automatic hyperparameter search for MLP and CNN was performed, and the best models were reported. For both task types, several metrics were calculated under a validation scheme to assess the effect of the prediction method and other variables. Overall, MLP and CNN presented competitive results to GBLUP. Also, we bring new insights on automated machine learning for genomic prediction and its implications to plant breeding.
546			_aText in English
650		7	_aAccuracy _2AGROVOC _927100
650		7	_aMaize _2AGROVOC _91173
650		7	_aHybrids _2AGROVOC _91151
650		7	_aPlant breeding _gAGROVOC _2 _91203
700	1		_aSabadin, F. _921706
700	1		_aYassue, R.M. _927101
700	1		_aGalves, C: _927102
700	1		_aFanelli Carvalho, H. _919807
700	1		_aCrossa, J. _gGenetic Resources Program _8CCJL01 _959
700	1		_aMontesinos-Lopez, O.A. _8I1706800 _92700 _gGenetic Resources Program
700	1		_aFritsche-Neto, R. _96507
773	0		_tFrontiers in Plant Science _gv. 13, art. 845524 _dSwitzerland : Frontiers, 2022 _w56875 _x1664-462X
856			_yOpen Access through DSpace _uhttps://hdl.handle.net/10883/22042
942			_cJA _n0 _2ddc
999			_c65173 _d65165