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L M A Barroso

Showing results (1-10 of 9) with videos related to

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Genetics and Molecular Research : GMR|May 14, 2016
Adaptability and phenotypic stability of common bean genotypes through Bayesian inferenceA M Corrêa, P E Teodoro, M C Gonçalves, et al.
Genetics and Molecular Research : GMR|May 14, 2016
Artificial intelligence in the selection of common bean genotypes with high phenotypic stabilityA M Corrêa, P E Teodoro, M C Gonçalves, et al.
Genetics and Molecular Research : GMR|May 14, 2016
Measurements of experimental precision for trials with cowpea (Vigna unguiculata L. Walp.) genotypesP E Teodoro, F E Torres, A D Santos, et al.
Genetics and Molecular Research : GMR|June 21, 2016
Bayesian forecasting of temporal gene expression by using an autoregressive panel data approachM Nascimento, F F E Silva, T Sáfadi, et al.
Genetics and Molecular Research : GMR|November 8, 2016
Using artificial neural networks to select upright cowpea (Vigna unguiculata) genotypes with high productivity and phenotypic stabilityL M A Barroso, P E Teodoro, M Nascimento, et al.
Genetics and Molecular Research : GMR|March 25, 2017
Regularized quantile regression applied to genome-enabled prediction of quantitative traitsM Nascimento, F F E Silva, M D V de Resende, et al.
Genetics and Molecular Research : GMR|March 18, 2016
Bayesian approach increases accuracy when selecting cowpea genotypes with high adaptability and phenotypic stabilityL M A Barroso, P E Teodoro, M Nascimento, et al.
Journal of Animal Science and Biotechnology|July 14, 2017
Regularized quantile regression for SNP marker estimation of pig growth curvesL M A Barroso, M Nascimento, A C C Nascimento, et al.
Genetics and Molecular Research : GMR|June 21, 2016
Factor analysis applied to genome prediction for high-dimensional phenotypes in pigsF R F Teixeira, M Nascimento, A C C Nascimento, et al.
Pageof 1

Showing results (1-10 of 9) with videos related to

Sort By:
Pageof 1
Genetics and Molecular Research : GMR|May 14, 2016
Adaptability and phenotypic stability of common bean genotypes through Bayesian inferenceA M Corrêa, P E Teodoro, M C Gonçalves, et al.
Genetics and Molecular Research : GMR|May 14, 2016
Artificial intelligence in the selection of common bean genotypes with high phenotypic stabilityA M Corrêa, P E Teodoro, M C Gonçalves, et al.
Genetics and Molecular Research : GMR|May 14, 2016
Measurements of experimental precision for trials with cowpea (Vigna unguiculata L. Walp.) genotypesP E Teodoro, F E Torres, A D Santos, et al.
Genetics and Molecular Research : GMR|June 21, 2016
Bayesian forecasting of temporal gene expression by using an autoregressive panel data approachM Nascimento, F F E Silva, T Sáfadi, et al.
Genetics and Molecular Research : GMR|November 8, 2016
Using artificial neural networks to select upright cowpea (Vigna unguiculata) genotypes with high productivity and phenotypic stabilityL M A Barroso, P E Teodoro, M Nascimento, et al.
Genetics and Molecular Research : GMR|March 25, 2017
Regularized quantile regression applied to genome-enabled prediction of quantitative traitsM Nascimento, F F E Silva, M D V de Resende, et al.
Genetics and Molecular Research : GMR|March 18, 2016
Bayesian approach increases accuracy when selecting cowpea genotypes with high adaptability and phenotypic stabilityL M A Barroso, P E Teodoro, M Nascimento, et al.
Journal of Animal Science and Biotechnology|July 14, 2017
Regularized quantile regression for SNP marker estimation of pig growth curvesL M A Barroso, M Nascimento, A C C Nascimento, et al.
Genetics and Molecular Research : GMR|June 21, 2016
Factor analysis applied to genome prediction for high-dimensional phenotypes in pigsF R F Teixeira, M Nascimento, A C C Nascimento, et al.
Pageof 1