000			03424nab a22004337a 4500
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003			MX-TxCIM
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022			_a2160-1836 (Online)
024	8		_2https://doi.org/10.1093/g3journal/jkad045
040			_aMX-TxCIM
041	0		_aeng
100	1		_aMontesinos-Lopez, A. _92702
245	1	0	_aMultimodal deep learning methods enhance genomic prediction of wheat breeding
260			_c2023. _aBethesda, MD (USA) : _bGenetics Society of America,
500			_aPeer review
500			_aOpen Access
520			_aWhile several statistical machine learning methods have been developed and studied for assessing the genomic prediction (GP) accuracy of unobserved phenotypes in plant breeding research, few methods have linked genomics and phenomics (imaging). Deep learning (DL) neural networks have been developed to increase the GP accuracy of unobserved phenotypes while simultaneously accounting for the complexity of genotype–environment interaction (GE); however, unlike conventional GP models, DL has not been investigated for when genomics is linked with phenomics. In this study we used 2 wheat data sets (DS1 and DS2) to compare a novel DL method with conventional GP models. Models fitted for DS1 were GBLUP, gradient boosting machine (GBM), support vector regression (SVR) and the DL method. Results indicated that for 1 year, DL provided better GP accuracy than results obtained by the other models. However, GP accuracy obtained for other years indicated that the GBLUP model was slightly superior to the DL. DS2 is comprised only of genomic data from wheat lines tested for 3 years, 2 environments (drought and irrigated) and 2–4 traits. DS2 results showed that when predicting the irrigated environment with the drought environment, DL had higher accuracy than the GBLUP model in all analyzed traits and years. When predicting drought environment with information on the irrigated environment, the DL model and GBLUP model had similar accuracy. The DL method used in this study is novel and presents a strong degree of generalization as several modules can potentially be incorporated and concatenated to produce an output for a multi-input data structure.
546			_aText in English
591			_aMontesinos-Lopez, O.A. : No CIMMYT Affiliation
650		7	_91310 _aWheat _2AGROVOC
650		7	_91029 _aBreeding _2AGROVOC
650		7	_911127 _aMachine learning _2AGROVOC
650		7	_91178 _aMethods _2AGROVOC
650		7	_910737 _aMarker-assisted selection _2AGROVOC
700	1		_91898 _aRivera, C. _8N1313814
700	1		_aPinto Espinosa, F. _8I1707012 _gFormerly Global Wheat Program _94431
700	1		_91901 _aPiñera Chavez, F.J. _8N1707052 _gGlobal Wheat Program
700	1		_926628 _aGonzález-Diéguez, D.O. _81707522 _gGlobal Wheat Program
700	1		_9831 _aReynolds, M.P. _8INT1511 _gGlobal Wheat Program
700	1		_92703 _aPerez-Rodriguez, P.
700	1		_9764 _aHuihui Li _8CLIH01 _gGenetic Resources Program
700	1		_92700 _aMontesinos-Lopez, O.A. _8I1706800 _gGenetic Resources Program
700	1		_aCrossa, J. _gGenetic Resources Program _8CCJL01 _959
773	0		_tG3: Genes, Genomes, Genetics _gv. 13, no. 5, art. jkad045 _dBethesda, MD (USA) : Genetics Society of America, 2023. _x2160-1836 _wu56922
856	4		_yOpen Access through DSpace _uhttps://hdl.handle.net/10883/22606
942			_cJA _2ddc _n0
999			_c66247 _d66239