320 related articles for article (PubMed ID: 34482627)
21. Evaluation of methods and marker Systems in Genomic Selection of oil palm (Elaeis guineensis Jacq.).
Kwong QB; Teh CK; Ong AL; Chew FT; Mayes S; Kulaveerasingam H; Tammi M; Yeoh SH; Appleton DR; Harikrishna JA
BMC Genet; 2017 Dec; 18(1):107. PubMed ID: 29228905
[TBL] [Abstract][Full Text] [Related]
22. Accelerating wheat breeding for end-use quality through association mapping and multivariate genomic prediction.
Zhang-Biehn S; Fritz AK; Zhang G; Evers B; Regan R; Poland J
Plant Genome; 2021 Nov; 14(3):e20164. PubMed ID: 34817128
[TBL] [Abstract][Full Text] [Related]
23. Accounting for Correlation Between Traits in Genomic Prediction.
Montesinos-López OA; Montesinos-López A; Mosqueda-Gonzalez BA; Montesinos-López JC; Crossa J
Methods Mol Biol; 2022; 2467():285-327. PubMed ID: 35451780
[TBL] [Abstract][Full Text] [Related]
24. A Benchmarking Between Deep Learning, Support Vector Machine and Bayesian Threshold Best Linear Unbiased Prediction for Predicting Ordinal Traits in Plant Breeding.
Montesinos-López OA; Martín-Vallejo J; Crossa J; Gianola D; Hernández-Suárez CM; Montesinos-López A; Juliana P; Singh R
G3 (Bethesda); 2019 Feb; 9(2):601-618. PubMed ID: 30593512
[TBL] [Abstract][Full Text] [Related]
25. Effectiveness of Genomic Selection by Response to Selection for Winter Wheat Variety Improvement.
Hu X; Carver BF; Powers C; Yan L; Zhu L; Chen C
Plant Genome; 2019 Nov; 12(3):1-15. PubMed ID: 33016592
[TBL] [Abstract][Full Text] [Related]
26. Genomic selection in wheat: optimum allocation of test resources and comparison of breeding strategies for line and hybrid breeding.
Longin CF; Mi X; Würschum T
Theor Appl Genet; 2015 Jul; 128(7):1297-306. PubMed ID: 25877519
[TBL] [Abstract][Full Text] [Related]
27. Genomic Selection for Processing and End-Use Quality Traits in the CIMMYT Spring Bread Wheat Breeding Program.
Battenfield SD; Guzmán C; Gaynor RC; Singh RP; Peña RJ; Dreisigacker S; Fritz AK; Poland JA
Plant Genome; 2016 Jul; 9(2):. PubMed ID: 27898810
[TBL] [Abstract][Full Text] [Related]
28. Genomic Prediction with Pedigree and Genotype × Environment Interaction in Spring Wheat Grown in South and West Asia, North Africa, and Mexico.
Sukumaran S; Crossa J; Jarquin D; Lopes M; Reynolds MP
G3 (Bethesda); 2017 Feb; 7(2):481-495. PubMed ID: 27903632
[TBL] [Abstract][Full Text] [Related]
29. Multi-trait genomic prediction using in-season physiological parameters increases prediction accuracy of complex traits in US wheat.
Shahi D; Guo J; Pradhan S; Khan J; Avci M; Khan N; McBreen J; Bai G; Reynolds M; Foulkes J; Babar MA
BMC Genomics; 2022 Apr; 23(1):298. PubMed ID: 35413795
[TBL] [Abstract][Full Text] [Related]
30. Comparison of genomic selection models for exploring predictive ability of complex traits in breeding programs.
Merrick LF; Carter AH
Plant Genome; 2021 Nov; 14(3):e20158. PubMed ID: 34719886
[TBL] [Abstract][Full Text] [Related]
31. Multi-trait, Multi-environment Deep Learning Modeling for Genomic-Enabled Prediction of Plant Traits.
Montesinos-López OA; Montesinos-López A; Crossa J; Gianola D; Hernández-Suárez CM; Martín-Vallejo J
G3 (Bethesda); 2018 Dec; 8(12):3829-3840. PubMed ID: 30291108
[TBL] [Abstract][Full Text] [Related]
32. Genomic Prediction for Grain Yield and Yield-Related Traits in Chinese Winter Wheat.
Ali M; Zhang Y; Rasheed A; Wang J; Zhang L
Int J Mol Sci; 2020 Feb; 21(4):. PubMed ID: 32079240
[TBL] [Abstract][Full Text] [Related]
33. Genomic prediction using machine learning: a comparison of the performance of regularized regression, ensemble, instance-based and deep learning methods on synthetic and empirical data.
Lourenço VM; Ogutu JO; Rodrigues RAP; Posekany A; Piepho HP
BMC Genomics; 2024 Feb; 25(1):152. PubMed ID: 38326768
[TBL] [Abstract][Full Text] [Related]
34. Multimodal deep learning methods enhance genomic prediction of wheat breeding.
Montesinos-López A; Rivera C; Pinto F; Piñera F; Gonzalez D; Reynolds M; Pérez-Rodríguez P; Li H; Montesinos-López OA; Crossa J
G3 (Bethesda); 2023 May; 13(5):. PubMed ID: 36869747
[TBL] [Abstract][Full Text] [Related]
35. Simultaneous selection for grain yield and protein content in genomics-assisted wheat breeding.
Michel S; Löschenberger F; Ametz C; Pachler B; Sparry E; Bürstmayr H
Theor Appl Genet; 2019 Jun; 132(6):1745-1760. PubMed ID: 30810763
[TBL] [Abstract][Full Text] [Related]
36. Applications of Machine Learning Methods to Genomic Selection in Breeding Wheat for Rust Resistance.
González-Camacho JM; Ornella L; Pérez-Rodríguez P; Gianola D; Dreisigacker S; Crossa J
Plant Genome; 2018 Jul; 11(2):. PubMed ID: 30025028
[TBL] [Abstract][Full Text] [Related]
37. Genomic Prediction and Genome-Wide Association Studies of Flour Yield and Alveograph Quality Traits Using Advanced Winter Wheat Breeding Material.
Kristensen PS; Jensen J; Andersen JR; Guzmán C; Orabi J; Jahoor A
Genes (Basel); 2019 Aug; 10(9):. PubMed ID: 31480460
[TBL] [Abstract][Full Text] [Related]
38. Comparison of Models and Whole-Genome Profiling Approaches for Genomic-Enabled Prediction of Septoria Tritici Blotch, Stagonospora Nodorum Blotch, and Tan Spot Resistance in Wheat.
Juliana P; Singh RP; Singh PK; Crossa J; Rutkoski JE; Poland JA; Bergstrom GC; Sorrells ME
Plant Genome; 2017 Jul; 10(2):. PubMed ID: 28724084
[TBL] [Abstract][Full Text] [Related]
39. Multi-trait genome prediction of new environments with partial least squares.
Montesinos-López OA; Montesinos-López A; Bernal Sandoval DA; Mosqueda-Gonzalez BA; Valenzo-Jiménez MA; Crossa J
Front Genet; 2022; 13():966775. PubMed ID: 36134027
[TBL] [Abstract][Full Text] [Related]
40. Genome-Wide Association and Prediction of Grain and Semolina Quality Traits in Durum Wheat Breeding Populations.
Fiedler JD; Salsman E; Liu Y; Michalak de Jiménez M; Hegstad JB; Chen B; Manthey FA; Chao S; Xu S; Elias EM; Li X
Plant Genome; 2017 Nov; 10(3):. PubMed ID: 29293807
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]