T. Zhao

Extend Mixed Models to Multi-layer Neural Networks for Genomic Prediction Including Intermediate Omics Data

J. Zeng, Hao Cheng, T. Zhao, 2021, bioRxiv.

Interpretable artificial neural networks incorporating Bayesian alphabet models for genome-wide prediction and association studies

R. Fernando, Hao Cheng, T. Zhao, 2021, bioRxiv.

Learning functional conservation between pig and human to decipher evolutionary mechanisms underlying gene expression and complex trait

J. Zeng, Zhili Zheng, Z. Pan, 2023, bioRxiv.

Fast parallelized sampling of Bayesian regression models for whole-genome prediction

R. Fernando, D. Garrick, Hao Cheng, 2020, Genetics Selection Evolution.

363. JWAS version 2: leveraging biological information and highthroughput phenotypes into genomic prediction and association

R. Fernando, D. Garrick, J. Qu, 2022, Proceeding of 12th World Congress on Genetics Applied to Livestock Production (WCGALP).

Interpreting single-step genomic evaluation as a neural network of three layers: pedigree, genotypes, and phenotypes

Hao Cheng, T. Zhao, 2023, Genetics Selection Evolution.

Interpreting single-step genomic evaluations as mixed effects neural networks of three layers: pedigree, genotypes, and phenotypes

Hao Cheng, Tianjing Zhao, T. Zhao, 2022, bioRxiv.

Fast parallelized sampling of Bayesian regression models for whole-genome prediction

R. Fernando, D. Garrick, Hao Cheng, 2020, Genetics Selection Evolution.

Using encrypted genotypes and phenotypes for collaborative genomic analyses to maintain data confidentiality

Hao Cheng, T. Zhao, Fangyi Wang, 2023, bioRxiv.

Learning functional conservation between human and pig to decipher evolutionary mechanisms underlying gene expression and complex traits

Zhili Zheng, Z. Pan, Zhe Zhang, 2023, Cell genomics.