000			02106nab a22003977a 4500
001			G98566
003			MX-TxCIM
005			20240919020947.0
008			121211b |||p||p||||||| |z||| |
022			_a1525-3163 (Revista en electrónico)
022	0		_a0021-8812
024	8		_ahttps://doi.org/10.2527/jas.2012-6162
040			_aMX-TxCIM
041	0		_aEn
100	1		_92703 _aPerez-Rodriguez, P.
245	0	0	_aTechnical Note: _b An R package for fitting Bayesian regularized neural networks with applications in animal breeding
260			_c2013
500			_aPeer-review: Yes - Open Access: Yes|http://science.thomsonreuters.com/cgi-bin/jrnlst/jlresults.cgi?PC=MASTER&ISSN=0021-8812
520			_aIn recent years, several statistical models have been developed for predicting genetic values for complex traits using information on dense molecular markers, pedigrees or both. These models include, among others, the Bayesian Regularized Neural Networks (BRNN) that have been widely used in prediction problems in other fields of application and, more recently, for genome-enabled prediction. The R brnn package described here implements BRNN models and extends these to include both additive and dominance effects. The implementation takes advantage of multicore architectures via a parallel computing approach using openMP (Open Multiprocessing) for the computations. This note briefly describes the classes of models that can be fitted using the brnn package, and it also illustrates its use through several real examples.
536			_aGenetic Resources Program
546			_aEnglish
593			_aLucia Segura
594			_aCCJL01
595			_aCSC
650	1	0	_aAnimal model
650	1	0	_aBRNN
650	1	0	_adominance and additive effects
650	1	0	_aGenomic selection _91513
650	1	0	_anon-parametric models
700	1		_aGianola, D., _ecoaut.
700	1		_aRosa, G.J.M., _ecoaut.
700	1		_aWeigel, K.A., _ecoaut.
700	1		_aCrossa, J. _gGenetic Resources Program _8CCJL01 _959
773	0		_tJournal of Animal Science _gv. 91, no. 8, p. 3522-3531
942			_cJA
999			_c30436 _d30436