© 2022 AIAOS Lab. This website used the open-source template on Github.
See the complete publication list on Google Scholar.
Tang Z, Wang J, De Castro A, Zhang Y, Primo VB, Montevecchio Bernardino AB, Morota G, Wang X, Chebel RC, and Yu H. (2026). Can 3D point cloud data improve automated body condition score prediction in dairy cattle? arXiv. doi: 10.48550/arXiv.2601.22522.
Wang J, De Castro A, Zhang Y, Basolli Borsatto L, Guo Y, Primo VB, Montevecchio Bernardino AB, Morota G, Chebel RC, and Yu H. (2026). Evaluating transfer learning strategies for improving dairy cattle body weight prediction in small farms using depth-image and point-cloud data. arXiv. doi: 10.48550/arXiv.2601.01044.
Watson MT, Feldmann M, Yu H, and Cheng H. (2025). End-to-end genomic prediction: Direct prediction of images and text from genome-wide molecular markers. bioRxiv. doi: 10.1101/2025.11.03.686395.
Niño de Guzmán C, Pinedo P, Yu H, Bliznyuk N, and De Vries A. (2026). Estimation of probability of pregnancy based on health status and estrus intensity in organic dairy cows. Dairy. doi: 10.3390/dairy7040058.
Anas M, Zhao B, Yu H, Dahlen C, Swanson KC, Ringwall KA, and Hanna LLH. (2026). Genome-wide association study of reproductive, body size, and carcass-related latent and directly measured traits in admixed beef heifers. Frontiers in Genetics. doi: 10.3389/fgene.2026.1653878.
Bi Y, Huang Y, Yu H, and Morota G. (2026). Impact of trait measurement error on quantitative genetic analysis of computer vision derived traits. Genes. doi: 10.3390/genes17050506.
Wang K, Choi D, Adams D, Ahn J, Balmant K, Dias R, Messina C, Munoz-Carpena R, Whitaker V, Yu H, Yu Z, Zhao C, and Li C. (2025). Artificial intelligence-powered plant phenomics: Progress, challenges, and opportunities. The Plant Phenome Journal. doi: 10.1002/ppj2.70060.
Wang J, Yu Z, Chebel RC, and Yu H. (2025). Impact of cross-validation designs on cattle behavior prediction using machine learning and deep learning models with tri-axial accelerometer data. Smart Agricultural Technology. doi: 10.1016/j.atech.2025.101483.
Chebel RC, Mirzaei A, Yu H, Lopes G Jr, and Bisinotto RS. (2025). Early postpartum estrous characteristics: Unveiling their predictive potential for fertility in dairy cows. Journal of Dairy Science. doi: 10.3168/jds.2025-27175.
Anas M, Zhao B, Yu H, Dahlen C, Swanson KC, Ringwall KA, Hulsman Hanna LL. (2025). Multi-Trait Phenotypic Modeling Through Factor Analysis and Bayesian Network Learning to Develop Latent Reproductive, Body Conformational, and Carcass-Associated Traits in Admixed Beef Heifers. Frontiers in Genetics. doi: 10.3389/fgene.2025.1551967.
De Castro A, Wang J, Bonney-King JG, Morota G, Miller-Cushon EK, and Yu, H. (2025). AnimalMotionViz: an interactive software tool for tracking and visualizing animal motion patterns using computer vision. JDS Communications. doi: 10.3168/jdsc.2024-0706
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. (2025). Multi-omics integration and immune profiling identify possible causal networks leading to uterine microbiome dysbiosis in dairy cows that develop metritis. Animal Microbiome. doi: 10.1186/s42523-024-00366-9
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
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, Godinho RM, Dekkers JCM, and Fernando RL. (2026). A method to compute genomic window variances for a multiple-regression model using posterior samples of effects from an equivalent dimension-reduced model. In: Proceedings, 13th World Congress on Genetics Applied to Livestock Production. July 12-17, Madison, WI.
Watson MT, Feldmann M, Yu H, and Cheng H. (2026). End-to-end genomic prediction: Predicting images and text from genome-wide molecular markers. In: Proceedings, 13th World Congress on Genetics Applied to Livestock Production. July 12-17, Madison, WI.
De Castro A, Chebel RC, and Yu H. (2026). Utility of multi-modal integration for predicting postpartum diseases in dairy cattle with imbalanced data. In: Proceedings, 13th World Congress on Genetics Applied to Livestock Production. July 12-17, Madison, WI.
De Castro A, Wang J, Bonney-King JG, Morota G, Miller-Cushon EK, and Yu H. (2025). AnimalMotionViz: An interactive software tool for tracking and visualizing animal motion patterns using computer vision. In: Proceedings, The 3rd US Conference on Precision Livestock Farming. June 2-5, Lincoln, NE.
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 on Genetics Applied to Livestock Production. July 3-8, Rotterdam, The Netherlands.
Dekkers JCM, Su H, Kramer L, and Yu H. (2022). An approach for the design of breeding programs using genomics. In: Proceedings, 12th World Congress on Genetics Applied to Livestock Production. July 3-8, Rotterdam, The Netherlands.
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 on Genetics Applied to Livestock Production. July 3-8, Rotterdam, The Netherlands.
Yu H, Spangler ML, Lewis RM, and Morota G. (2018). Stronger measures of genomic connectedness enhance prediction accuracies across management units. In: Proceedings, 11th World Congress on Genetics Applied to Livestock Production. 11:406. February 11-16, Auckland, New Zealand.
© 2022 AIAOS Lab. This website used the open-source template on Github.