Predicting expected progeny difference for marbling score in Angus cattle using artificial neural networks and Bayesian regression models. - Wikidata - awgldk - TriplyDB

Predicting expected progeny difference for marbling score in Angus cattle using artificial neural networks and Bayesian regression models.

Predicting expected progeny difference for marbling score in Angus cattle using artificial neural networks and Bayesian regression models.

http://www.wikidata.org/entity/Q37364405

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Optimal Design of Low-Density SNP Arrays for Genomic Prediction: Algorithm and Applications Application of neural networks with back-propagation to genome-enabled prediction of complex traits in Holstein-Friesian and German Fleckvieh cattle Accuracy of Igenity genomically estimated breeding values for predicting Australian Angus BREEDPLAN traits.

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Predicting expected progeny difference for marbling score in Angus cattle using artificial neural networks and Bayesian regression models.

http://www.wikidata.org/entity/Q37364405

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article científic

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article scientifique

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articolo scientifico

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artigo científico

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bilimsel makale

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scientific article published on 11 September 2013

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vedecký článok

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vetenskaplig artikel

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videnskabelig artikel

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vědecký článek

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name

Predicting expected progeny di ...... nd Bayesian regression models.

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type

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Predicting expected progeny di ...... nd Bayesian regression models.

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Predicting expected progeny di ...... nd Bayesian regression models.

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prefLabel

Predicting expected progeny di ...... nd Bayesian regression models.

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Predicting expected progeny di ...... nd Bayesian regression models.

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Genetics Selection Evolution

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Predicting expected progeny di ...... nd Bayesian regression models.

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Brent W Woodward

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Daniel Gianola

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Guilherme J M Rosa

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Hayrettin Okut

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Jeremy F Taylor

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Stewart Bauck

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Xiao-Liao Wu

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P2860

Parallel Markov chain Monte Carlo - bridging the gap to high-performance Bayesian computation in animal breeding and genetics

Design of a bovine low-density SNP array optimized for imputation

A primer on high-throughput computing for genomic selection

Prediction of total genetic value using genome-wide dense marker maps

Artificial neural networks for linkage analysis of quantitative gene expression phenotypes and evaluation of gene x gene interactions.

Deregressing estimated breeding values and weighting information for genomic regression analyses.

Extension of the bayesian alphabet for genomic selection.

Predicting complex quantitative traits with Bayesian neural networks: a case study with Jersey cows and wheat.

Detecting, characterizing, and interpreting nonlinear gene-gene interactions using multifactor dimensionality reduction.

Bayesian approach for neural networks--review and case studies.

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Robert D. Schnabel

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