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Journal Abstract Search

238 related items for PubMed ID: 32738177

  • 1. Incorporation of parental phenotypic data into multi-omic models improves prediction of yield-related traits in hybrid rice.
    Xu Y, Zhao Y, Wang X, Ma Y, Li P, Yang Z, Zhang X, Xu C, Xu S.
    Plant Biotechnol J; 2021 Feb; 19(2):261-272. PubMed ID: 32738177
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  • 3. Metabolomic prediction of yield in hybrid rice.
    Xu S, Xu Y, Gong L, Zhang Q.
    Plant J; 2016 Oct; 88(2):219-227. PubMed ID: 27311694
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  • 4. A directed learning strategy integrating multiple omic data improves genomic prediction.
    Hu X, Xie W, Wu C, Xu S.
    Plant Biotechnol J; 2019 Oct; 17(10):2011-2020. PubMed ID: 30950198
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  • 5. Hybrid breeding of rice via genomic selection.
    Cui Y, Li R, Li G, Zhang F, Zhu T, Zhang Q, Ali J, Li Z, Xu S.
    Plant Biotechnol J; 2020 Jan; 18(1):57-67. PubMed ID: 31124256
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  • 7. Predicting rice hybrid performance using univariate and multivariate GBLUP models based on North Carolina mating design II.
    Wang X, Li L, Yang Z, Zheng X, Yu S, Xu C, Hu Z.
    Heredity (Edinb); 2017 Mar; 118(3):302-310. PubMed ID: 27649618
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  • 9. Improvement of prediction ability by integrating multi-omic datasets in barley.
    Wu PY, Stich B, Weisweiler M, Shrestha A, Erban A, Westhoff P, Inghelandt DV.
    BMC Genomics; 2022 Mar 12; 23(1):200. PubMed ID: 35279073
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  • 11. Genomic selection of agronomic traits in hybrid rice using an NCII population.
    Xu Y, Wang X, Ding X, Zheng X, Yang Z, Xu C, Hu Z.
    Rice (N Y); 2018 May 10; 11(1):32. PubMed ID: 29748895
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  • 12. Genomic Prediction of Yield Traits in Single-Cross Hybrid Rice (Oryza sativa L.).
    Labroo MR, Ali J, Aslam MU, de Asis EJ, Dela Paz MA, Sevilla MA, Lipka AE, Studer AJ, Rutkoski JE.
    Front Genet; 2021 May 10; 12():692870. PubMed ID: 34276796
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  • 15. Predicted Residual Error Sum of Squares of Mixed Models: An Application for Genomic Prediction.
    Xu S.
    G3 (Bethesda); 2017 Mar 10; 7(3):895-909. PubMed ID: 28108552
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