Xu Wang

This study uses deep learning and genome-wide association studies to identify pest-resistant genes in grapevine, achieving high accuracy in predicting pest damage.

The paper tests an extended omnigenic model to explain the geometric distribution of effect sizes in complex traits using sorghum yield data, finding evidence for regulatory effects both in space and time.

The paper tests genetic variation for limited transpiration in sorghum under field conditions and evaluates canopy temperature as a surrogate trait.

This study uses phenomics and genomics to improve early-season chilling tolerance in sorghum through marker-assisted breeding, showing up to 24% performance improvement in lines with the CT allele.

This study uses high-throughput phenotyping and genomic prediction to assess and improve barley yellow dwarf resistance in wheat.

This study evaluates the economic benefits of genomic selection in a winter wheat breeding program by analyzing prediction accuracy, genotyping costs, and heritability levels.

This paper uses deep learning to score wheat morphology and flowering time from field images, revealing novel genetic interactions and heritable flowering rates.