285 related articles for article (PubMed ID: 31533955)
1. Benchmarking Parametric and Machine Learning Models for Genomic Prediction of Complex Traits.
Azodi CB; Bolger E; McCarren A; Roantree M; de Los Campos G; Shiu SH
G3 (Bethesda); 2019 Nov; 9(11):3691-3702. PubMed ID: 31533955
[TBL] [Abstract][Full Text] [Related]
2. Using machine learning to realize genetic site screening and genomic prediction of productive traits in pigs.
Xiang T; Li T; Li J; Li X; Wang J
FASEB J; 2023 Jun; 37(6):e22961. PubMed ID: 37178007
[TBL] [Abstract][Full Text] [Related]
3. 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]
4. Application of neural networks with back-propagation to genome-enabled prediction of complex traits in Holstein-Friesian and German Fleckvieh cattle.
Ehret A; Hochstuhl D; Gianola D; Thaller G
Genet Sel Evol; 2015 Mar; 47(1):22. PubMed ID: 25886037
[TBL] [Abstract][Full Text] [Related]
5. Genomic Prediction and Association Mapping of Curd-Related Traits in Gene Bank Accessions of Cauliflower.
Thorwarth P; Yousef EAA; Schmid KJ
G3 (Bethesda); 2018 Feb; 8(2):707-718. PubMed ID: 29255118
[TBL] [Abstract][Full Text] [Related]
6. A Comparison of Three Machine Learning Methods for Multivariate Genomic Prediction Using the Sparse Kernels Method (SKM) Library.
Montesinos-López OA; Montesinos-López A; Cano-Paez B; Hernández-Suárez CM; Santana-Mancilla PC; Crossa J
Genes (Basel); 2022 Aug; 13(8):. PubMed ID: 36011405
[TBL] [Abstract][Full Text] [Related]
7. 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]
8. Accounting for trait architecture in genomic predictions of US Holstein cattle using a weighted realized relationship matrix.
Tiezzi F; Maltecca C
Genet Sel Evol; 2015 Apr; 47(1):24. PubMed ID: 25886167
[TBL] [Abstract][Full Text] [Related]
9. Accuracy of genomic selection for a sib-evaluated trait using identity-by-state and identity-by-descent relationships.
Vela-Avitúa S; Meuwissen TH; Luan T; Ødegård J
Genet Sel Evol; 2015 Feb; 47(1):9. PubMed ID: 25888184
[TBL] [Abstract][Full Text] [Related]
10. Prediction performance of linear models and gradient boosting machine on complex phenotypes in outbred mice.
Perez BC; Bink MCAM; Svenson KL; Churchill GA; Calus MPL
G3 (Bethesda); 2022 Apr; 12(4):. PubMed ID: 35166767
[TBL] [Abstract][Full Text] [Related]
11. Genomic prediction in plants: opportunities for ensemble machine learning based approaches.
Farooq M; van Dijk ADJ; Nijveen H; Mansoor S; de Ridder D
F1000Res; 2022; 11():802. PubMed ID: 37035464
[No Abstract] [Full Text] [Related]
12. Genomic Prediction of Additive and Non-additive Effects Using Genetic Markers and Pedigrees.
de Almeida Filho JE; Guimarães JFR; Fonsceca E Silva F; Vilela de Resende MD; Muñoz P; Kirst M; de Resende Júnior MFR
G3 (Bethesda); 2019 Aug; 9(8):2739-2748. PubMed ID: 31263059
[TBL] [Abstract][Full Text] [Related]
13. A Bayesian Genomic Multi-output Regressor Stacking Model for Predicting Multi-trait Multi-environment Plant Breeding Data.
Montesinos-López OA; Montesinos-López A; Crossa J; Cuevas J; Montesinos-López JC; Gutiérrez ZS; Lillemo M; Philomin J; Singh R
G3 (Bethesda); 2019 Oct; 9(10):3381-3393. PubMed ID: 31427455
[TBL] [Abstract][Full Text] [Related]
14. A comparative study of 11 non-linear regression models highlighting autoencoder, DBN, and SVR, enhanced by SHAP importance analysis in soybean branching prediction.
Zhou W; Yan Z; Zhang L
Sci Rep; 2024 Mar; 14(1):5905. PubMed ID: 38467662
[TBL] [Abstract][Full Text] [Related]
15. Genomic Prediction for Quantitative Traits Is Improved by Mapping Variants to Gene Ontology Categories in Drosophila melanogaster.
Edwards SM; Sørensen IF; Sarup P; Mackay TF; Sørensen P
Genetics; 2016 Aug; 203(4):1871-83. PubMed ID: 27235308
[TBL] [Abstract][Full Text] [Related]
16. Impact of multi-output and stacking methods on feed efficiency prediction from genotype using machine learning algorithms.
Mora M; González P; Quevedo JR; Montañés E; Tusell L; Bergsma R; Piles M
J Anim Breed Genet; 2023 Nov; 140(6):638-652. PubMed ID: 37403756
[TBL] [Abstract][Full Text] [Related]
17. Genomic predictions for economically important traits in Brazilian Braford and Hereford beef cattle using true and imputed genotypes.
Piccoli ML; Brito LF; Braccini J; Cardoso FF; Sargolzaei M; Schenkel FS
BMC Genet; 2017 Jan; 18(1):2. PubMed ID: 28100165
[TBL] [Abstract][Full Text] [Related]
18. 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]
19. An Equation to Predict the Accuracy of Genomic Values by Combining Data from Multiple Traits, Populations, or Environments.
Wientjes YC; Bijma P; Veerkamp RF; Calus MP
Genetics; 2016 Feb; 202(2):799-823. PubMed ID: 26637542
[TBL] [Abstract][Full Text] [Related]
20. Accuracies of genomic predictions for disease resistance of striped catfish to Edwardsiella ictaluri using artificial intelligence algorithms.
Vu NT; Phuc TH; Oanh KTP; Sang NV; Trang TT; Nguyen NH
G3 (Bethesda); 2022 Jan; 12(1):. PubMed ID: 34788431
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]