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Stewart Bauck

Showing results (1-10 of 14) with videos related to

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Yi Chuan = Hereditas|April 29, 2018
[Estimating genomic breed composition of individual animals using selected SNPs]Jun He, Chang Song Qian, Richard G Tait, et al.
Yi Chuan = Hereditas|July 17, 2019
[Impacts of SNP genotyping call rate and SNP genotyping error rate on imputation accuracy inHolsteincattle]Zhi Li, Jun He, Jun Jiang, et al.
Frontiers in Genetics|June 30, 2020
Estimation of Genomic Breed Composition for Purebred and Crossbred Animals Using Sparsely Regularized Admixture ModelsYangfan Wang, Xiao-Lin Wu, Zhi Li, et al.
BMC Genetics|August 11, 2018
Comparing SNP panels and statistical methods for estimating genomic breed composition of individual animals in ten cattle breedsJun He, Yage Guo, Jiaqi Xu, et al.
Genetica|December 16, 2017
Comparing strategies for selection of low-density SNPs for imputation-mediated genomic prediction in U. S. HolsteinsJun He, Jiaqi Xu, Xiao-Lin Wu, et al.
Genetics, Selection, Evolution : GSE|September 13, 2013
Predicting expected progeny difference for marbling score in Angus cattle using artificial neural networks and Bayesian regression modelsHayrettin Okut, Xiao-Liao Wu, Guilherme J M Rosa, et al.
Frontiers in Genetics|February 4, 2012
A primer on high-throughput computing for genomic selectionXiao-Lin Wu, Timothy M Beissinger, Stewart Bauck, et al.
Plos One|August 16, 2020
Genomic mating as sustainable breeding for Chinese indigenous Ningxiang pigsJun He, Xiao-Lin Wu, Qinghua Zeng, et al.
Frontiers in Genetics|April 14, 2023
A look under the hood of genomic-estimated breed compositions for brangus cattle: What have we learned?Zhi Li, Jun He, Fang Yang, et al.
Genetics Research|July 20, 2012
An ensemble-based approach to imputation of moderate-density genotypes for genomic selection with application to Angus cattleChuanyu Sun, Xiao-Lin Wu, Kent A Weigel, et al.
Pageof 2

Showing results (1-10 of 14) with videos related to

Sort By:
Pageof 2
Yi Chuan = Hereditas|April 29, 2018
[Estimating genomic breed composition of individual animals using selected SNPs]Jun He, Chang Song Qian, Richard G Tait, et al.
Yi Chuan = Hereditas|July 17, 2019
[Impacts of SNP genotyping call rate and SNP genotyping error rate on imputation accuracy inHolsteincattle]Zhi Li, Jun He, Jun Jiang, et al.
Frontiers in Genetics|June 30, 2020
Estimation of Genomic Breed Composition for Purebred and Crossbred Animals Using Sparsely Regularized Admixture ModelsYangfan Wang, Xiao-Lin Wu, Zhi Li, et al.
BMC Genetics|August 11, 2018
Comparing SNP panels and statistical methods for estimating genomic breed composition of individual animals in ten cattle breedsJun He, Yage Guo, Jiaqi Xu, et al.
Genetica|December 16, 2017
Comparing strategies for selection of low-density SNPs for imputation-mediated genomic prediction in U. S. HolsteinsJun He, Jiaqi Xu, Xiao-Lin Wu, et al.
Genetics, Selection, Evolution : GSE|September 13, 2013
Predicting expected progeny difference for marbling score in Angus cattle using artificial neural networks and Bayesian regression modelsHayrettin Okut, Xiao-Liao Wu, Guilherme J M Rosa, et al.
Frontiers in Genetics|February 4, 2012
A primer on high-throughput computing for genomic selectionXiao-Lin Wu, Timothy M Beissinger, Stewart Bauck, et al.
Plos One|August 16, 2020
Genomic mating as sustainable breeding for Chinese indigenous Ningxiang pigsJun He, Xiao-Lin Wu, Qinghua Zeng, et al.
Frontiers in Genetics|April 14, 2023
A look under the hood of genomic-estimated breed compositions for brangus cattle: What have we learned?Zhi Li, Jun He, Fang Yang, et al.
Genetics Research|July 20, 2012
An ensemble-based approach to imputation of moderate-density genotypes for genomic selection with application to Angus cattleChuanyu Sun, Xiao-Lin Wu, Kent A Weigel, et al.
Pageof 2