231 related articles for article (PubMed ID: 33469463)
1. Deep Learning for Predicting Complex Traits in Spring Wheat Breeding Program.
Sandhu KS; Lozada DN; Zhang Z; Pumphrey MO; Carter AH
Front Plant Sci; 2020; 11():613325. PubMed ID: 33469463
[TBL] [Abstract][Full Text] [Related]
2. Multitrait machine- and deep-learning models for genomic selection using spectral information in a wheat breeding program.
Sandhu K; Patil SS; Pumphrey M; Carter A
Plant Genome; 2021 Nov; 14(3):e20119. PubMed ID: 34482627
[TBL] [Abstract][Full Text] [Related]
3. Genomic Selection for End-Use Quality and Processing Traits in Soft White Winter Wheat Breeding Program with Machine and Deep Learning Models.
Sandhu KS; Aoun M; Morris CF; Carter AH
Biology (Basel); 2021 Jul; 10(7):. PubMed ID: 34356544
[TBL] [Abstract][Full Text] [Related]
4. Multi-Trait Multi-Environment Genomic Prediction for End-Use Quality Traits in Winter Wheat.
Sandhu KS; Patil SS; Aoun M; Carter AH
Front Genet; 2022; 13():831020. PubMed ID: 35173770
[TBL] [Abstract][Full Text] [Related]
5. Ridge regression and deep learning models for genome-wide selection of complex traits in New Mexican Chile peppers.
Lozada DN; Sandhu KS; Bhatta M
BMC Genom Data; 2023 Dec; 24(1):80. PubMed ID: 38110866
[TBL] [Abstract][Full Text] [Related]
6. Combining Genomic and Phenomic Information for Predicting Grain Protein Content and Grain Yield in Spring Wheat.
Sandhu KS; Mihalyov PD; Lewien MJ; Pumphrey MO; Carter AH
Front Plant Sci; 2021; 12():613300. PubMed ID: 33643347
[TBL] [Abstract][Full Text] [Related]
7. TrG2P: A transfer-learning-based tool integrating multi-trait data for accurate prediction of crop yield.
Li J; Zhang D; Yang F; Zhang Q; Pan S; Zhao X; Zhang Q; Han Y; Yang J; Wang K; Zhao C
Plant Commun; 2024 May; ():100975. PubMed ID: 38751121
[TBL] [Abstract][Full Text] [Related]
8. Deep learning versus parametric and ensemble methods for genomic prediction of complex phenotypes.
Abdollahi-Arpanahi R; Gianola D; Peñagaricano F
Genet Sel Evol; 2020 Feb; 52(1):12. PubMed ID: 32093611
[TBL] [Abstract][Full Text] [Related]
9. Enhancing the potential of phenomic and genomic prediction in winter wheat breeding using high-throughput phenotyping and deep learning.
Kaushal S; Gill HS; Billah MM; Khan SN; Halder J; Bernardo A; Amand PS; Bai G; Glover K; Maimaitijiang M; Sehgal SK
Front Plant Sci; 2024; 15():1410249. PubMed ID: 38872880
[TBL] [Abstract][Full Text] [Related]
10. DNNGP, a deep neural network-based method for genomic prediction using multi-omics data in plants.
Wang K; Abid MA; Rasheed A; Crossa J; Hearne S; Li H
Mol Plant; 2023 Jan; 16(1):279-293. PubMed ID: 36366781
[TBL] [Abstract][Full Text] [Related]
11. 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]
12. Optimizing neural networks for medical data sets: A case study on neonatal apnea prediction.
Shirwaikar RD; Acharya U D; Makkithaya K; M S; Srivastava S; Lewis U LES
Artif Intell Med; 2019 Jul; 98():59-76. PubMed ID: 31521253
[TBL] [Abstract][Full Text] [Related]
13. Genomic Selection in Winter Wheat Breeding Using a Recommender Approach.
Lozada DN; Carter AH
Genes (Basel); 2020 Jul; 11(7):. PubMed ID: 32664601
[TBL] [Abstract][Full Text] [Related]
14. Efficient mapping of crash risk at intersections with connected vehicle data and deep learning models.
Hu J; Huang MC; Yu X
Accid Anal Prev; 2020 Sep; 144():105665. PubMed ID: 32683130
[TBL] [Abstract][Full Text] [Related]
15. Increased Predictive Accuracy of Multi-Environment Genomic Prediction Model for Yield and Related Traits in Spring Wheat (
Tomar V; Singh D; Dhillon GS; Chung YS; Poland J; Singh RP; Joshi AK; Gautam Y; Tiwari BS; Kumar U
Front Plant Sci; 2021; 12():720123. PubMed ID: 34691100
[TBL] [Abstract][Full Text] [Related]
16. Deep learning methods improve genomic prediction of wheat breeding.
Montesinos-López A; Crespo-Herrera L; Dreisigacker S; Gerard G; Vitale P; Saint Pierre C; Govindan V; Tarekegn ZT; Flores MC; Pérez-Rodríguez P; Ramos-Pulido S; Lillemo M; Li H; Montesinos-López OA; Crossa J
Front Plant Sci; 2024; 15():1324090. PubMed ID: 38504889
[TBL] [Abstract][Full Text] [Related]
17. 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]
18. Residual network improves the prediction accuracy of genomic selection.
Wu H; Gao B; Zhang R; Huang Z; Yin Z; Hu X; Yang CX; Du ZQ
Anim Genet; 2024 Aug; 55(4):599-611. PubMed ID: 38746973
[TBL] [Abstract][Full Text] [Related]
19. Detection and analysis of wheat spikes using Convolutional Neural Networks.
Hasan MM; Chopin JP; Laga H; Miklavcic SJ
Plant Methods; 2018; 14():100. PubMed ID: 30459822
[TBL] [Abstract][Full Text] [Related]
20. Improving Wheat Yield Prediction Using Secondary Traits and High-Density Phenotyping Under Heat-Stressed Environments.
Rahman MM; Crain J; Haghighattalab A; Singh RP; Poland J
Front Plant Sci; 2021; 12():633651. PubMed ID: 34646280
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]