These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

169 related articles for article (PubMed ID: 20479144)

  • 1. Graph-based data selection for the construction of genomic prediction models.
    Maenhout S; De Baets B; Haesaert G
    Genetics; 2010 Aug; 185(4):1463-75. PubMed ID: 20479144
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Phenotypic Data from Inbred Parents Can Improve Genomic Prediction in Pearl Millet Hybrids.
    Liang Z; Gupta SK; Yeh CT; Zhang Y; Ngu DW; Kumar R; Patil HT; Mungra KD; Yadav DV; Rathore A; Srivastava RK; Gupta R; Yang J; Varshney RK; Schnable PS; Schnable JC
    G3 (Bethesda); 2018 Jul; 8(7):2513-2522. PubMed ID: 29794163
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Genome properties and prospects of genomic prediction of hybrid performance in a breeding program of maize.
    Technow F; Schrag TA; Schipprack W; Bauer E; Simianer H; Melchinger AE
    Genetics; 2014 Aug; 197(4):1343-55. PubMed ID: 24850820
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Resource allocation for maximizing prediction accuracy and genetic gain of genomic selection in plant breeding: a simulation experiment.
    Lorenz AJ
    G3 (Bethesda); 2013 Mar; 3(3):481-91. PubMed ID: 23450123
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Genomic prediction of hybrid performance in maize with models incorporating dominance and population specific marker effects.
    Technow F; Riedelsheimer C; Schrag TA; Melchinger AE
    Theor Appl Genet; 2012 Oct; 125(6):1181-94. PubMed ID: 22733443
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Maximizing efficiency of genomic selection in CIMMYT's tropical maize breeding program.
    Atanda SA; Olsen M; Burgueño J; Crossa J; Dzidzienyo D; Beyene Y; Gowda M; Dreher K; Zhang X; Prasanna BM; Tongoona P; Danquah EY; Olaoye G; Robbins KR
    Theor Appl Genet; 2021 Jan; 134(1):279-294. PubMed ID: 33037897
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Genomic prediction models for traits differing in heritability for soybean, rice, and maize.
    Kaler AS; Purcell LC; Beissinger T; Gillman JD
    BMC Plant Biol; 2022 Feb; 22(1):87. PubMed ID: 35219296
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Genomic Prediction of Complex Traits in an Allogamous Annual Crop: The Case of Maize Single-Cross Hybrids.
    Martins Oliveira IC; Bernardeli A; Soler Guilhen JH; Pastina MM
    Methods Mol Biol; 2022; 2467():543-567. PubMed ID: 35451790
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Optimal Designs for Genomic Selection in Hybrid Crops.
    Guo T; Yu X; Li X; Zhang H; Zhu C; Flint-Garcia S; McMullen MD; Holland JB; Szalma SJ; Wisser RJ; Yu J
    Mol Plant; 2019 Mar; 12(3):390-401. PubMed ID: 30625380
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Molecular marker-based prediction of hybrid performance in maize using unbalanced data from multiple experiments with factorial crosses.
    Schrag TA; Möhring J; Maurer HP; Dhillon BS; Melchinger AE; Piepho HP; Sørensen AP; Frisch M
    Theor Appl Genet; 2009 Feb; 118(4):741-51. PubMed ID: 19048224
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Prediction of maize single-cross hybrid performance: support vector machine regression versus best linear prediction.
    Maenhout S; De Baets B; Haesaert G
    Theor Appl Genet; 2010 Jan; 120(2):415-27. PubMed ID: 19904522
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Genome-Assisted Prediction of Quantitative Traits Using the R Package sommer.
    Covarrubias-Pazaran G
    PLoS One; 2016; 11(6):e0156744. PubMed ID: 27271781
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Marker genotyping error effects on genomic predictions under different genetic architectures.
    Akbarpour T; Ghavi Hossein-Zadeh N; Shadparvar AA
    Mol Genet Genomics; 2021 Jan; 296(1):79-89. PubMed ID: 32995954
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Power of in silico QTL mapping from phenotypic, pedigree, and marker data in a hybrid breeding program.
    Yu J; Arbelbide M; Bernardo R
    Theor Appl Genet; 2005 Apr; 110(6):1061-7. PubMed ID: 15754207
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Impact of genotyping strategy on the accuracy of genomic prediction in simulated populations of purebred swine.
    Li X; Zhang Z; Liu X; Chen Y
    Animal; 2019 Sep; 13(9):1804-1810. PubMed ID: 30616709
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Imputation of unordered markers and the impact on genomic selection accuracy.
    Rutkoski JE; Poland J; Jannink JL; Sorrells ME
    G3 (Bethesda); 2013 Mar; 3(3):427-39. PubMed ID: 23449944
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Genotyping marker density and prediction models effects in long-term breeding schemes of cross-pollinated crops.
    DoVale JC; Carvalho HF; Sabadin F; Fritsche-Neto R
    Theor Appl Genet; 2022 Dec; 135(12):4523-4539. PubMed ID: 36261658
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Prediction of single-cross hybrid performance in maize using haplotype blocks associated with QTL for grain yield.
    Schrag TA; Maurer HP; Melchinger AE; Piepho HP; Peleman J; Frisch M
    Theor Appl Genet; 2007 May; 114(8):1345-55. PubMed ID: 17323040
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Genome-based prediction of testcross values in maize.
    Albrecht T; Wimmer V; Auinger HJ; Erbe M; Knaak C; Ouzunova M; Simianer H; Schön CC
    Theor Appl Genet; 2011 Jul; 123(2):339-50. PubMed ID: 21505832
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Genomic Prediction using Existing Historical Data Contributing to Selection in Biparental Populations: A Study of Kernel Oil in Maize.
    Hao Y; Wang H; Yang X; Zhang H; He C; Li D; Li H; Wang G; Wang J; Fu J
    Plant Genome; 2019 Mar; 12(1):. PubMed ID: 30951098
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 9.