Publications

See the publications on Google Scholar

• De Castro A, Wang J, Bonney-King JG, Morota G, Miller-Cushon EK, and Yu, H. (2024). AnimalMotionViz: an interactive software tool for tracking and visualizing animal motion patterns using computer vision. bioRxiv. doi: 10.1101/2024.10.22.619671

• Casaro S, Prim JG, Gonzalez TD, Cunha F, Silva ACM, Yu H, Bisinotto RS, Chebel RC, Santos JE, Nelson CD, Jeon SJ, Bicalho RC, Driver JP, and Galvão KN. (2024). Multi-omics integration and immune profiling identify possible causal networks leading to uterine microbiome dysbiosis in dairy cows that develop metritis. Research Square. doi: 10.21203/rs.3.rs-4571697

• Yan H, Jin Y, Yu H, Wang C, Wu B, Jone CS, Wang X, Xie Z, and Huang L. (2024). Genomic selection for agronomical phenotypes using genome-wide SNPs and SVs in pearl millet. Theoretical and Applied Genetics. doi: 10.1007/s00122-024-04754-2

• Yu H, Fernando RL, and Dekkers JCM. (2024). Use of the linear regression method to evaluate population accuracy of predictions from non-linear models. Frontiers in Genetics. doi: 10.3389/fgene.2024.1380643

• Marin MU, Gingerich KN, Wang J, Yu H, and Miller-Cushon EK. (2024). Effects of space allowance on patterns of activity in group-housed dairy calves. JDS Communications. doi: 10.3168/jdsc.2023-0486

• Wang J, Hu Y, Xiang L, Morota G, Brooks SA, Wickens CL, Miller-Cushon EK, and Yu, H. (2024). Technical note: ShinyAnimalCV: open-source cloud-based web application for object detection, segmentation, and three-dimensional visualization of animals using computer vision. Journal of Animal Science. doi: 10.1093/jas/skad416

• Bi Y, Campos LM, Wang J, Yu H, Hanigan MD, and Morota G. (2023). Depth video data-enabled predictions of longitudinal dairy cow body weight using thresholding and Mask R-CNN algorithms. Smart Agricultural Technology. doi: 10.1016/j.atech.2023.100352

• de Novais FJ, Yu H, Cesar ASM, Momen M, Poleti MD, Petry B, Mourao GB, de Almeida Re- gitano LC, Morota G, and Coutinho LL (2022). Multi-omic data integration for the study of production, carcass, and meat quality traits in Nellore cattle. Frontiers in Genetics. doi: 10.3389/fgene.2022.948240

• Yu H, van Milgen J, Knol EF, Fernando RL, and Dekkers JCM. (2022). A bayesian hierarchical model to integrate a mechanistic growth model in genomic prediction. In: Proceedings, 12th World Congress of Genetics Applied to Livestock Production. July 3-8, Rotterdam, The Netherlands. [PDF]

• Dekkers JCM, Su L, Kramer L, and Yu H. (2022). A tool for the design of breeding programs using genomics. In: Proceedings, 12th World Congress of Genetics Applied to Livestock Production. July 3-8, Rotterdam, The Netherlands. [PDF]

• Ni Z, Fernando RL, Yu H, Knol EF, Dekkers JCM. (2022). Genomic prediction of longitudinal body weights in pigs using a neural network. In: Proceedings, 12th World Congress of Genetics Applied to Livestock Production. July 3-8, Rotterdam, The Netherlands. [PDF]

• Clevinger EM, Biyashev R, Lerch-Olson E, Yu H, Quigley C, Song Q, Dorrance AE, Robertson AE, and Maroof S. (2021). Identification of Quantitative Disease Resistance Loci towards Four Pythium Species in Soybean. Frontiers in Plant Science. doi: 10.3389/fpls.2021.644746

• Pegolo S, Yu H, Morota G, Bisutti V, Rosa GJM, Bittante G, and Cecchinato A. (2021). Structural equation modelling for unravelling the multivariate genomic architecture of milk proteins in dairy cattle. Journal of Dairy Science. doi: 10.3168/jds.2020-18321

• Yu H and Morota G. (2021). GCA: An R package for genetic connectedness analysis using pedigree and genomic data. BMC Genomics. 22:119. doi: 10.1186/s12864-021-07414-7

• Yu H, Lee K, and Morota G. (2021). Forecasting dynamic body weight of non-restrained pigs from images using an RGB-D sensor camera. Translational Animal Science. 5:1-9. doi: 10.1093/tas/txab006

• Momen M, Bhatta M, Hussain W, Yu H, and Morota G. (2021). Modeling multiple phenotypes in wheat using data-driven genomic exploratory factor analysis and Bayesian network learning. Plant Direct. 00:e00304. doi: 10.1002/pld3.304

• Amorim ST, Yu H, Momen M, de Albuquerque, LG, Pereira, ASC, Baldi F, and Morota G. (2020). An assessment of genomic connectedness measures in Nellore cattle. Journal of Animal Science. 98:1-12. doi: 10.1093/jas/skaa289

• Yu H, Morota G, Celestino EF, Dahlen CR, Wagner SA, Riley DG, and Hanna LLH. (2020). Deciphering cattle temperament measures derived from a four-platform standing scale using genetic factor analytic modeling. Frontiers in Genetics. 11:599. doi: 10.3389/fgene.2020.00599

• Hanna LLH, Hieber JK, Yu H, Celestino Jr EF, Dahlen CR, Wagner SA, and Riley DG. (2019). Blood collection has negligible impact on scoring temperament in Angus-based weaned calves. Livestock Science. 230:103835. doi: 10.1016/j.livsci.2019.103835

• Yu H, Campbell MT, Zhang Q, Walia H, and Morota G. (2019). Genomic Bayesian confirmatory factor analysis and Bayesian network to characterize a wide spectrum of rice phenotypes. G3: Genes, Genomes, Genetics. 9:1975-1986. doi: 10.1534/g3.119.400154

• Yu H, Spangler ML, Lewis RM, and Morota G. (2018). Do stronger measures of genomic connectedness enhance prediction accuracies across management units? Journal of Animal Science. 96:4490-4500. doi: 10.1093/jas/sky316

• Yu H, Spangler ML, Lewis RM, and Morota G. (2017). Genomic relatedness strengthens genetic connectedness across management units. G3: Genes, Genomes, Genetics. 10:3543-3556. doi: 10.1534/g3.117.300151