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Genetics, selection, evolution : GSE

Showing results (681-690 of 1,616) with videos related to

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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.
Genetics, Selection, Evolution : GSE|June 7, 2013
Use of partial least squares regression to impute SNP genotypes in Italian cattle breedsCorrado Dimauro, Massimo Cellesi, Giustino Gaspa, et al.
Genetics, Selection, Evolution : GSE|July 10, 2013
Impact of prior specifications in a shrinkage-inducing Bayesian model for quantitative trait mapping and genomic predictionTimo Knürr, Esa Läärä, Mikko J Sillanpää
Genetics, Selection, Evolution : GSE|July 20, 2013
Assessment of residual body weight gain and residual intake and body weight gain as feed efficiency traits in the turkey (Meleagris gallopavo)Owen W Willems, Stephen P Miller, Benjamin J Wood
Genetics, Selection, Evolution : GSE|October 7, 2014
Genomic prediction based on runs of homozygosityTu Luan, Xijiang Yu, Marlies Dolezal, et al.
Genetics, Selection, Evolution : GSE|December 10, 2013
Inversion of a part of the numerator relationship matrix using pedigree informationPierre Faux, Nicolas Gengler
Genetics, Selection, Evolution : GSE|November 6, 2014
A comparison of principal component regression and genomic REML for genomic prediction across populationsChristos Dadousis, Roel F Veerkamp, Bjørg Heringstad, et al.
Genetics, Selection, Evolution : GSE|March 26, 2026
Genetic analysis of iris pigmentation in Swiss pig breeds identifies a missense KITLG variant as a potential causal factor for pale and heterochromatic irisesWim Gorssen, Naveen Kumar Kadri, Negar Khayatzadeh, et al.
Genetics, Selection, Evolution : GSE|June 4, 2026
Gsformer: a dual-architecture deep learning framework with CNN-self-attention and sparse-attention for genomic selectionTingting Yang, Weicong Wu, Yahui Xue, et al.
Genetics, Selection, Evolution : GSE|June 2, 2023
A method for partitioning trends in genetic mean and variance to understand breeding practicesThiago P Oliveira, Jana Obšteter, Ivan Pocrnic, et al.
Pageof 162

Showing results (681-690 of 1,616) with videos related to

Sort By:
Pageof 162
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.
Genetics, Selection, Evolution : GSE|June 7, 2013
Use of partial least squares regression to impute SNP genotypes in Italian cattle breedsCorrado Dimauro, Massimo Cellesi, Giustino Gaspa, et al.
Genetics, Selection, Evolution : GSE|July 10, 2013
Impact of prior specifications in a shrinkage-inducing Bayesian model for quantitative trait mapping and genomic predictionTimo Knürr, Esa Läärä, Mikko J Sillanpää
Genetics, Selection, Evolution : GSE|July 20, 2013
Assessment of residual body weight gain and residual intake and body weight gain as feed efficiency traits in the turkey (Meleagris gallopavo)Owen W Willems, Stephen P Miller, Benjamin J Wood
Genetics, Selection, Evolution : GSE|October 7, 2014
Genomic prediction based on runs of homozygosityTu Luan, Xijiang Yu, Marlies Dolezal, et al.
Genetics, Selection, Evolution : GSE|December 10, 2013
Inversion of a part of the numerator relationship matrix using pedigree informationPierre Faux, Nicolas Gengler
Genetics, Selection, Evolution : GSE|November 6, 2014
A comparison of principal component regression and genomic REML for genomic prediction across populationsChristos Dadousis, Roel F Veerkamp, Bjørg Heringstad, et al.
Genetics, Selection, Evolution : GSE|March 26, 2026
Genetic analysis of iris pigmentation in Swiss pig breeds identifies a missense KITLG variant as a potential causal factor for pale and heterochromatic irisesWim Gorssen, Naveen Kumar Kadri, Negar Khayatzadeh, et al.
Genetics, Selection, Evolution : GSE|June 4, 2026
Gsformer: a dual-architecture deep learning framework with CNN-self-attention and sparse-attention for genomic selectionTingting Yang, Weicong Wu, Yahui Xue, et al.
Genetics, Selection, Evolution : GSE|June 2, 2023
A method for partitioning trends in genetic mean and variance to understand breeding practicesThiago P Oliveira, Jana Obšteter, Ivan Pocrnic, et al.
Pageof 162