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 *

190 related articles for article (PubMed ID: 32020678)

  • 1. Improving genomic prediction accuracy for meat tenderness in Nellore cattle using artificial neural networks.
    Brito Lopes F; Magnabosco CU; Passafaro TL; Brunes LC; Costa MFO; Eifert EC; Narciso MG; Rosa GJM; Lobo RB; Baldi F
    J Anim Breed Genet; 2020 Sep; 137(5):438-448. PubMed ID: 32020678
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Accuracy of genomic breeding values for meat tenderness in Polled Nellore cattle.
    Magnabosco CU; Lopes FB; Fragoso RC; Eifert EC; Valente BD; Rosa GJ; Sainz RD
    J Anim Sci; 2016 Jul; 94(7):2752-60. PubMed ID: 27482662
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Accuracy of genomic predictions in Bos indicus (Nellore) cattle.
    Neves HH; Carvalheiro R; O'Brien AM; Utsunomiya YT; do Carmo AS; Schenkel FS; Sölkner J; McEwan JC; Van Tassell CP; Cole JB; da Silva MV; Queiroz SA; Sonstegard TS; Garcia JF
    Genet Sel Evol; 2014 Feb; 46(1):17. PubMed ID: 24575732
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Genomewide association mapping and pathway analysis of meat tenderness in Polled Nellore cattle.
    Castro LM; Rosa GJM; Lopes FB; Regitano LCA; Rosa AJM; Magnabosco CU
    J Anim Sci; 2017 May; 95(5):1945-1956. PubMed ID: 28727016
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Genomic prediction of breeding values for carcass traits in Nellore cattle.
    Fernandes Júnior GA; Rosa GJ; Valente BD; Carvalheiro R; Baldi F; Garcia DA; Gordo DG; Espigolan R; Takada L; Tonussi RL; de Andrade WB; Magalhães AF; Chardulo LA; Tonhati H; de Albuquerque LG
    Genet Sel Evol; 2016 Jan; 48():7. PubMed ID: 26830208
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Genome-enabled prediction of meat and carcass traits using Bayesian regression, single-step genomic best linear unbiased prediction and blending methods in Nelore cattle.
    Lopes FB; Baldi F; Passafaro TL; Brunes LC; Costa MFO; Eifert EC; Narciso MG; Rosa GJM; Lobo RB; Magnabosco CU
    Animal; 2021 Jan; 15(1):100006. PubMed ID: 33516009
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Genome-enable prediction for health traits using high-density SNP panel in US Holstein cattle.
    Lopes F; Rosa G; Pinedo P; Santos JEP; Chebel RC; Galvao KN; Schuenemann GM; Bicalho RC; Gilbert RO; Rodrigez-Zas S; Seabury CM; Thatcher W
    Anim Genet; 2020 Mar; 51(2):192-199. PubMed ID: 31909828
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Predicting expected progeny difference for marbling score in Angus cattle using artificial neural networks and Bayesian regression models.
    Okut H; Wu XL; Rosa GJ; Bauck S; Woodward BW; Schnabel RD; Taylor JF; Gianola D
    Genet Sel Evol; 2013 Sep; 45(1):34. PubMed ID: 24024641
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Genomic selection for meat quality traits in Nelore cattle.
    Magalhães AFB; Schenkel FS; Garcia DA; Gordo DGM; Tonussi RL; Espigolan R; Silva RMO; Braz CU; Fernandes Júnior GA; Baldi F; Carvalheiro R; Boligon AA; de Oliveira HN; Chardulo LAL; de Albuquerque LG
    Meat Sci; 2019 Feb; 148():32-37. PubMed ID: 30296711
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Accuracy of prediction of genomic breeding values for residual feed intake and carcass and meat quality traits in Bos taurus, Bos indicus, and composite beef cattle.
    Bolormaa S; Pryce JE; Kemper K; Savin K; Hayes BJ; Barendse W; Zhang Y; Reich CM; Mason BA; Bunch RJ; Harrison BE; Reverter A; Herd RM; Tier B; Graser HU; Goddard ME
    J Anim Sci; 2013 Jul; 91(7):3088-104. PubMed ID: 23658330
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Accuracy of prediction of simulated polygenic phenotypes and their underlying quantitative trait loci genotypes using real or imputed whole-genome markers in cattle.
    Hassani S; Saatchi M; Fernando RL; Garrick DJ
    Genet Sel Evol; 2015 Dec; 47():99. PubMed ID: 26698091
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Genome-wide association study and prediction of genomic breeding values for fatty-acid composition in Korean Hanwoo cattle using a high-density single-nucleotide polymorphism array.
    Bhuiyan MSA; Kim YK; Kim HJ; Lee DH; Lee SH; Yoon HB; Lee SH
    J Anim Sci; 2018 Sep; 96(10):4063-4075. PubMed ID: 30265318
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Accuracy of predicting genomic breeding values for residual feed intake in Angus and Charolais beef cattle.
    Chen L; Schenkel F; Vinsky M; Crews DH; Li C
    J Anim Sci; 2013 Oct; 91(10):4669-78. PubMed ID: 24078618
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Strategies for single nucleotide polymorphism (SNP) genotyping to enhance genotype imputation in Gyr (Bos indicus) dairy cattle: Comparison of commercially available SNP chips.
    Boison SA; Santos DJ; Utsunomiya AH; Carvalheiro R; Neves HH; O'Brien AM; Garcia JF; Sölkner J; da Silva MV
    J Dairy Sci; 2015 Jul; 98(7):4969-89. PubMed ID: 25958293
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Genomic prediction ability for beef fatty acid profile in Nelore cattle using different pseudo-phenotypes.
    Chiaia HLJ; Peripolli E; de Oliveira Silva RM; Feitosa FLB; de Lemos MVA; Berton MP; Olivieri BF; Espigolan R; Tonussi RL; Gordo DGM; de Albuquerque LG; de Oliveira HN; Ferrinho AM; Mueller LF; Kluska S; Tonhati H; Pereira ASC; Aguilar I; Baldi F
    J Appl Genet; 2018 Nov; 59(4):493-501. PubMed ID: 30251238
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Strategies for genotype imputation in composite beef cattle.
    Chud TC; Ventura RV; Schenkel FS; Carvalheiro R; Buzanskas ME; Rosa JO; Mudadu Mde A; da Silva MV; Mokry FB; Marcondes CR; Regitano LC; Munari DP
    BMC Genet; 2015 Aug; 16():99. PubMed ID: 26250698
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Genomic prediction for meat and carcass traits in Nellore cattle using a Markov blanket algorithm.
    Lopes FB; Baldi F; Brunes LC; Oliveira E Costa MF; da Costa Eifert E; Rosa GJM; Lobo RB; Magnabosco CU
    J Anim Breed Genet; 2023 Jan; 140(1):1-12. PubMed ID: 36239216
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Genomic prediction for tick resistance in Braford and Hereford cattle.
    Cardoso FF; Gomes CC; Sollero BP; Oliveira MM; Roso VM; Piccoli ML; Higa RH; Yokoo MJ; Caetano AR; Aguilar I
    J Anim Sci; 2015 Jun; 93(6):2693-705. PubMed ID: 26115257
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Design of a low-density SNP chip for the main Australian sheep breeds and its effect on imputation and genomic prediction accuracy.
    Bolormaa S; Gore K; van der Werf JH; Hayes BJ; Daetwyler HD
    Anim Genet; 2015 Oct; 46(5):544-56. PubMed ID: 26360638
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Accuracy of genome-wide imputation in Braford and Hereford beef cattle.
    Piccoli ML; Braccini J; Cardoso FF; Sargolzaei M; Larmer SG; Schenkel FS
    BMC Genet; 2014 Dec; 15():157. PubMed ID: 25543517
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 10.