000			03197nab a22004097a 4500
999			_c58994 _d58986
001			58994
003			MX-TxCIM
005			20240919020949.0
008			180105s2018 mdu|||p sp||| 00| 0 eng d
024	8		_ahttps://doi.org/10.1534/g3.117.300309
040			_aMX-TxCIM
041			_aeng
100	1		_92700 _aMontesinos-Lopez, O.A. _gGenetic Resources Program _8I1706800
245	1		_aPrediction of multiple-trait and multiple-environment genomic data using recommender systems _h[Electronic Resource]
260			_aBethesda, MD : _bGenetis Society of America, _c2018.
500			_aPeer review
500			_aOpen Access
520			_aIn genomic-enabled prediction, the task of improving the accuracy of the prediction of lines in environments is difficult because the available information is generally sparse and usually has low correlations between traits. In current genomic selection, although researchers have a large amount of information and appropriate statistical models to process it, there is still limited computing efficiency to do so. Although some statistical models are usually mathematically elegant, many of them are also computationally inefficient, and they are impractical for many traits, lines, environments, and years because they need to sample from huge normal multivariate distributions. For these reasons, this study explores two recommender systems: itembased collaborative filtering (IBCF) and the matrix factorization algorithm (MF) in the context of multiple traits and multiple environments. The IBCF and MF methods were compared with two conventional methods on simulated and real data. Results of the simulated and real data sets show that the IBCF technique was slightly better in terms of prediction accuracy than the two conventional methods and the MF method when the correlation was moderately high. The IBCF technique is very attractive because it produces good predictions when there is high correlation between items (environment–trait combinations) and its implementation is computationally feasible, which can be useful for plant breeders who deal with very large data sets.
526			_aWC _cFP2
546			_aText in English
650		7	_91132 _aGenomics _2AGROVOC
650		7	_91133 _aGenotype environment interaction _2AGROVOC
650		7	_92624 _aStatistical methods _2AGROVOC
650		7	_94619 _aPrecision agriculture _2AGROVOC
700	1		_92702 _aMontesinos-Lopez, A.
700	1		_aCrossa, J. _gGenetic Resources Program _8CCJL01 _959
700	1		_94950 _aMontesinos-Lopez, J.C.
700	1		_95853 _aMota-Sanchez, D.
700	1		_95854 _aEstrada-González, F.
700	1		_95855 _aGillberg, J.
700	1		_aSingh, R.P. _gGlobal Wheat Program _8INT0610 _9825
700	1		_aMondal, S. _gFormerly Global Wheat Program _8INT3211 _9904
700	1		_aJULIANA P. _8001710082 _gFormerly ​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​Global Wheat Program _gFormerly BISA _92690
773	0		_gv. 8, no. 1, p. 131-147 _tG3 _wu56922
856	4		_yOpen Access through DSpace _uhttps://hdl.handle.net/10883/19119
942			_2ddc _cJA _n0