Technical Note: An R package for fitting Bayesian regularized neural networks with applications in animal breeding
Perez-Rodriguez, P.
Technical Note: An R package for fitting Bayesian regularized neural networks with applications in animal breeding - 2013
Peer-review: Yes - Open Access: Yes|http://science.thomsonreuters.com/cgi-bin/jrnlst/jlresults.cgi?PC=MASTER&ISSN=0021-8812
In 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.
English
1525-3163 (Revista en electrónico) 0021-8812
https://doi.org/10.2527/jas.2012-6162
Animal model
BRNN
dominance and additive effects
Genomic selection
non-parametric models
Technical Note: An R package for fitting Bayesian regularized neural networks with applications in animal breeding - 2013
Peer-review: Yes - Open Access: Yes|http://science.thomsonreuters.com/cgi-bin/jrnlst/jlresults.cgi?PC=MASTER&ISSN=0021-8812
In 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.
English
1525-3163 (Revista en electrónico) 0021-8812
https://doi.org/10.2527/jas.2012-6162
Animal model
BRNN
dominance and additive effects
Genomic selection
non-parametric models