Haipeng Yu

Assistant Professor

University of Florida

About me

I am an Assistant Professor in Artificial Intelligence in Animal Omics Sciences in the Department of Animal Sciences at the University of Florida. My research group (AIAOS) primarily focuses on integrating high-dimensional heterogeneous -omics data to advance agriculture using statistical modeling, machine learning, and computational methods.

Interests

Quantitative Genetics
Statistical Genetics
Growth Model
Computer Vision
Bayesian Machine Learning

Education

Ph.D., Quantitative Genetics, 2020

Virginia Tech
M.S., Animal Breeding and Genetics, 2016

North Dakota State University
B.S., Veterinary Medicine, 2013

Qingdao Agricultural University

Publications

More Publications

Haipeng Yu and Gota Morota. GCA: An R package for genetic connectedness analysis using pedigree and genomic data. BMC Genomics, 2021.

DOI bioRxiv

Elizabeth M. Clevinger, Ruslan Biyashev, Elizabeth Lerch-Olson, Haipeng Yu, Charles Quigley, Qijian Song, Anne E. Dorrance, Alison E. Robertson, and M.A. Saghai Maroof. Identification of Quantitative Disease Resistance Loci towards Four Pythium Species in Soybean. Frontiers in Plant Science, 2021.

DOI

Sara Pegolo, Haipeng Yu, Gota Morota, Vittoria Bisutti, Guilherme J. M. Rosa, Giovanni Bittante, and Alessio Cecchinato. Structural equation modelling for unravelling the multivariate genomic architecture of milk proteins in dairy cattle. Journal of Dairy Science, 2021.

DOI

Haipeng Yu, Kiho Lee, and Gota Morota. Forecasting dynamic body weight of non-restrained pigs from images using an RGB-D sensor camera. Translational Animal Science, 2021.

DOI

Mehdi Momen, Madhav Bhatta, Waseem Hussain, Haipeng Yu, and Gota Morota. Modeling multiple phenotypes in wheat using data-driven genomic exploratory factor analysis and Bayesian network learning. Plant Direct, 2021.

DOI bioRxiv

Sabrina T. Amorim, Haipeng Yu, Mehdi Momen, Lúcia Galvão de Albuquerque, Angélica S. Cravo Pereira, Fernando Baldi, and Gota Morota. An assessment of genomic connectedness measures in Nellore cattle. Journal of Animal Science, 2020.

DOI PubMed EuropePMC

Haipeng Yu, Gota Morota, Elfren F. Celestino Jr, Carl R. Dahlen, Sarah A. Wagner, David G. Riley, and Lauren L. Hulsman Hanna. Deciphering cattle temperament measures derived from a four-platform standing scale using genetic factor analytic modeling. Frontiers in Genetics, 2020.

DOI PubMed EuropePMC bioRxiv

Lauren L. Hulsman Hanna, Jordan K. Hieber, Haipeng Yu, Elfren F. Celestino Jr, Carl R. Dahlen, Sarah A. Wagner, and David G. Riley. Blood collection has negligible impact on scoring temperament in Angus-based weaned calves. Livestock Science, 2019.

DOI

Haipeng Yu, Malachy Campbell, Qi Zhang, Harkamal Walia, and Gota Morota. Genomic Bayesian confirmatory factor analysis and Bayesian network to characterize a wide spectrum of rice phenotypes. G3: Genes, Genomes, Genetics, 2019.

DOI PubMed EuropePMC bioRxiv

Haipeng Yu, Matthew L. Spangler, Ronald M. Lewis, and Gota Morota. Do stronger measures of genomic connectedness enhance prediction accuracies across management units?. Journal of Animal Science, 2018.

DOI PubMed EuropePMC

Software

R package
- GCA: An R package for genetic connectedness analysis across units using pedigree and genomic data. [Github Source] [Documentation]

Teaching

Guest Instructor

Virginia Tech
- GWAS Workshop (Summer 2019) [Slides] [Workshop Website]
Univeristy of Nebraska-Lincoln
- ASCI 944/STAT 844 Quantitative Methods for Genomics of Complex Traits (Spring 2018) [Slides] [WWW]

Graduate Teaching Assistant

Virginia Tech
- APSC 5984: Complex Trait Genomics (Spring 2020) [WWW]
- ALS 3104: Animal Breeding and Genetics (Spring 2019)
North Dakota State University
- ANSC 357: Animal Genetics (Spring 2016)
- AGRI 189: Skills for Academic Success (Fall 2015)

Tutorials

Factor Analytic Model [WWW]
Gaussian Bayesian Network [WWW]
Structural Equation Model GWAS [WWW]