Research

1. Integration of -omics data for genetic improvement using statistical modeling and machine learning

• 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

• Yu, H, van Milgen, J, Knol, E. F, Fernando, R. L, and Dekkers, J. C. (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]

• Ni Z, Fernando RL, Yu H, Knol EF, and 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]

• 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

• 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

• 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

• 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

2. AI-assisted precision livestock farming

• 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

• 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

• 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

3. Open-source software development for animal omics sciences

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

• 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

• 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]

• 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