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Genome-enabled prediction of genetic values using radial basis function neural networks

Overview of attention for article published in Theoretical & Applied Genetics, May 2012
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1 tweeter

Citations

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110 Mendeley
Title
Genome-enabled prediction of genetic values using radial basis function neural networks
Published in
Theoretical & Applied Genetics, May 2012
DOI 10.1007/s00122-012-1868-9
Pubmed ID
Authors

J. M. González-Camacho, G. de los Campos, P. Pérez, D. Gianola, J. E. Cairns, G. Mahuku, R. Babu, J. Crossa

Abstract

The availability of high density panels of molecular markers has prompted the adoption of genomic selection (GS) methods in animal and plant breeding. In GS, parametric, semi-parametric and non-parametric regressions models are used for predicting quantitative traits. This article shows how to use neural networks with radial basis functions (RBFs) for prediction with dense molecular markers. We illustrate the use of the linear Bayesian LASSO regression model and of two non-linear regression models, reproducing kernel Hilbert spaces (RKHS) regression and radial basis function neural networks (RBFNN) on simulated data and real maize lines genotyped with 55,000 markers and evaluated for several trait-environment combinations. The empirical results of this study indicated that the three models showed similar overall prediction accuracy, with a slight and consistent superiority of RKHS and RBFNN over the additive Bayesian LASSO model. Results from the simulated data indicate that RKHS and RBFNN models captured epistatic effects; however, adding non-signal (redundant) predictors (interaction between markers) can adversely affect the predictive accuracy of the non-linear regression models.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 110 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Mexico 3 3%
Brazil 1 <1%
Australia 1 <1%
United States 1 <1%
Netherlands 1 <1%
Unknown 103 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 32 29%
Researcher 30 27%
Student > Doctoral Student 12 11%
Student > Master 10 9%
Unspecified 6 5%
Other 20 18%
Readers by discipline Count As %
Agricultural and Biological Sciences 82 75%
Computer Science 8 7%
Unspecified 7 6%
Biochemistry, Genetics and Molecular Biology 6 5%
Veterinary Science and Veterinary Medicine 1 <1%
Other 6 5%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 27 July 2012.
All research outputs
#2,304,939
of 4,507,280 outputs
Outputs from Theoretical & Applied Genetics
#167
of 432 outputs
Outputs of similar age
#35,056
of 75,206 outputs
Outputs of similar age from Theoretical & Applied Genetics
#3
of 5 outputs
Altmetric has tracked 4,507,280 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 432 research outputs from this source. They receive a mean Attention Score of 2.0. This one is in the 46th percentile – i.e., 46% of its peers scored the same or lower than it.
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We're also able to compare this research output to 5 others from the same source and published within six weeks on either side of this one. This one has scored higher than 2 of them.