Papers
5
Total Citations
122
H-Index
4
About
Shiyi Chen is an emerging force in quantitative and genomic genetics, with research centered on dairy cattle breeding, precision livestock farming, and the application of advanced computational methods to animal science. Chen's work has made significant contributions to understanding the genetic architecture of complex traits in North American Holstein cattle, particularly in the context of resilience, milkability, udder conformation, and behavioral characteristics derived from automatic milking systems (AMS). Among Chen's most impactful contributions is pioneering the estimation of genomic-based genetic parameters for animal resilience across lactations—a timely and critical area given mounting pressures from climate change and evolving animal welfare standards (46 citations). Chen has also advanced the field by leveraging AMS-generated data to derive novel genetic parameters for milkability (36 citations) and three-dimensional udder conformation traits using Cartesian coordinates (20 citations), transforming raw robotic data into actionable breeding tools. Particularly noteworthy is Chen's integration of machine learning and deep learning algorithms for genomic prediction of behavioral traits (18 citations), bridging traditional quantitative genetics with modern artificial intelligence. Collectively, Chen's body of work is reshaping how precision technologies inform sustainable dairy cattle improvement programs.
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