| Extend mixed models to multilayer neural networks for genomic prediction including intermediate omics data T Zhao, J Zeng, H Cheng Genetics 221 (1), iyac034, 2022 | 21 | 2022 |
| Interpretable artificial neural networks incorporating Bayesian alphabet models for genome-wide prediction and association studies T Zhao, R Fernando, H Cheng G3 11 (10), jkab228, 2021 | 17 | 2021 |
| Fast parallelized sampling of Bayesian regression models for whole-genome prediction T Zhao, R Fernando, D Garrick, H Cheng Genetics Selection Evolution 52 (1), 16, 2020 | 9 | 2020 |
| Learning functional conservation between human and pig to decipher evolutionary mechanisms underlying gene expression and complex traits J Li, T Zhao, D Guan, Z Pan, Z Bai, J Teng, Z Zhang, Z Zheng, J Zeng, ... Cell Genomics 3 (10), 2023 | 7 | 2023 |
| Using encrypted genotypes and phenotypes for collaborative genomic analyses to maintain data confidentiality T Zhao, F Wang, R Mott, J Dekkers, H Cheng Genetics 226 (3), iyad210, 2024 | 6 | 2024 |
| JWAS version 2: leveraging biological information and highthroughput phenotypes into genomic prediction and association H Cheng, R Fernando, D Garrick, T Zhao, J Qu Proceedings of 12th World Congress on Genetics Applied to Livestock …, 2022 | 5 | 2022 |
| Microbiome-enabled genomic selection improves prediction accuracy for nitrogen-related traits in maize Z Yang, T Zhao, H Cheng, J Yang G3: Genes, Genomes, Genetics 14 (3), jkad286, 2024 | | 2024 |
| Interpreting single-step genomic evaluation as a neural network of three layers: pedigree, genotypes, and phenotypes T Zhao, H Cheng Genetics Selection Evolution 55 (1), 68, 2023 | | 2023 |
| Challenges in Whole-Genome Analysis: Multilayer Omics Data and Data Encryption T Zhao University of California, Davis, 2023 | | 2023 |
| Quantifying the functional conservation between human and pig using artificial neural networks J Li, T Zhao, Z Pan, H Zhou, L Fang, H Cheng Proceedings of 12th World Congress on Genetics Applied to Livestock …, 2022 | | 2022 |
| ShinyJWAS: Shiny-based Application to Help Perform Whole-genome Bayesian Regression Analysis with JWAS package Z Wang, T Zhao, H Cheng | | |
Hao ChengUniversity of California, Davis在 ucdavis.edu 的电子邮件经过验证
Hao ChengUniversity of California, Davis在 ucdavis.edu 的电子邮件经过验证
Rohan FernandoIowa State University在 iastate.edu 的电子邮件经过验证