| Genomic bayesian confirmatory factor analysis and bayesian network to characterize a wide spectrum of rice phenotypes H Yu, MT Campbell, Q Zhang, H Walia, G Morota G3: Genes, Genomes, Genetics 9 (6), 1975-1986, 2019 | 17 | 2019 |
| Genomic relatedness strengthens genetic connectedness across management units H Yu, ML Spangler, RM Lewis, G Morota G3: Genes, Genomes, Genetics 7 (10), 3543-3556, 2017 | 16 | 2017 |
| Deciphering cattle temperament measures derived from a four-platform standing scale using genetic factor analytic modeling H Yu, G Morota, EF Celestino Jr, CR Dahlen, SA Wagner, DG Riley, ... Frontiers in genetics 11, 599, 2020 | 13 | 2020 |
| Do stronger measures of genomic connectedness enhance prediction accuracies across management units? H Yu, ML Spangler, RM Lewis, G Morota Journal of animal science 96 (11), 4490-4500, 2018 | 10 | 2018 |
| Structural equation modeling for unraveling the multivariate genomic architecture of milk proteins in dairy cattle S Pegolo, H Yu, G Morota, V Bisutti, GJM Rosa, G Bittante, A Cecchinato Journal of Dairy Science 104 (5), 5705-5718, 2021 | 9 | 2021 |
| Forecasting dynamic body weight of nonrestrained pigs from images using an RGB-D sensor camera H Yu, K Lee, G Morota Translational Animal Science 5 (1), txab006, 2021 | 8 | 2021 |
| GCA: an R package for genetic connectedness analysis using pedigree and genomic data H Yu, G Morota BMC genomics 22 (1), 1-8, 2021 | 6 | 2021 |
| Identification of Quantitative Disease Resistance Loci Toward Four Pythium Species in Soybean EM Clevinger, R Biyashev, E Lerch-Olson, H Yu, C Quigley, Q Song, ... Frontiers in Plant Science 12, 644746, 2021 | 6 | 2021 |
| Modeling multiple phenotypes in wheat using data‐driven genomic exploratory factor analysis and Bayesian network learning M Momen, M Bhatta, W Hussain, H Yu, G Morota Plant Direct 5 (1), e00304, 2021 | 6 | 2021 |
| The exploration of a four-platform standing scale in the application of measuring temperament in beef cattle H Yu North Dakota State University, 2016 | 6 | 2016 |
| Blood collection has negligible impact on scoring temperament in angus-based weaned calves LLH Hanna, JK Hieber, H Yu, EF Celestino Jr, CR Dahlen, SA Wagner, ... Livestock Science 230, 103835, 2019 | 5 | 2019 |
| Examining the relationships between phenotypic plasticity and local environments with genomic structural equation models MT Campbell, H Yu, M Momen, G Morota bioRxiv, 2019.12. 11.873257, 2019 | 2 | 2019 |
| A Bayesian hierarchical model to integrate a mechanistic growth model in genomic prediction H Yu, J van Milgen, E Knol, R Fernando, J Dekkers 12. World congress on genetics applied to livestock production (WCGALP), 2022 | 1 | 2022 |
| An assessment of genomic connectedness measures in Nellore cattle ST Amorim, H Yu, M Momen, LG de Albuquerque, AS Cravo Pereira, ... Journal of animal science 98 (11), skaa289, 2020 | 1 | 2020 |
| Impact of blood collection on scoring temperament in Angus-based weaned calves is negligible LLH Hanna, JK Hieber, H Yu, CR Dahlen, SA Wagner, DG Riley Journal of Animal Science 95, 19, 2017 | 1 | 2017 |
| An approach for the design of breeding programs using genomics JCM Dekkers, H Su, L Kramer, H Yu Proceeding of 12th World Congress on Genetics Applied to Livestock …, 2022 | | 2022 |
| Multi-omic data integration for the study of production, carcass, and meat quality traits in Nellore cattle FJ Novais, H Yu, ASM Cesar, M Momen, MD Poleti, B Petry, GB Mourão, ... Frontiers in Genetics, 3029, 2022 | | 2022 |
| Validation of the linear regression method to evaluate population accuracy and bias of predictions for non-linear models H Yu, RL Fernando, JCM Dekkers bioRxiv, 2022.10. 02.510518, 2022 | | 2022 |
| 242 Development of image analysis pipeline to predict body weight in pigs H Yu, K Lee, G Morota Journal of Animal Science 98 (Supplement_4), 177-178, 2020 | | 2020 |
| Designing and modeling high-throughput phenotyping data in quantitative genetics H Yu Virginia Tech, 2020 | | 2020 |
Gota MorotaVirginia Polytechnic Institute and State University,在 vt.edu 的电子邮件经过验证
