Author

Zhanyou Xu

Agricultural Research ServiceORCID 0000-0002-7633-036XUnclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Approach:machine learning (classical)· 2 Organism:plant (other)· 2 Approach:statistical model· 2 Research Focus:benchmarking· 1 Research Focus:database / resource· 1 Research Focus:fine-mapping· 1 Research Focus:gene regulatory network inference· 1 Research Focus:GWAS / association mapping· 1

Frequent Co-Authors

Adam L. MahaniD· 1 shared Andreomar José Kurek· 1 shared Cesar Augusto MedinaiD· 1 shared Craig Thomas BeiliD· 1 shared Deborah Jo HeuscheleiD· 1 shared Dongyan ZhaoiD· 1 shared L. M. MirandaiD· 1 shared Meng LiniD· 1 shared

Activity by Year

2020-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 17 mutual co-author links

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1 shared paperPlant Biology

Adam L. MahanORCID

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3 papers

Recent·Most cited

Genome-Wide Association Mapping of Sudden Death Syndrome Resistance in Germplasm from the USDA Soybean Collection

L. M. Miranda, Adam L. Mahan, Zhanyou Xu

This study identifies seven SNPs and three QTL associated with SDS resistance in soybean using GWAS on 351 USDA germplasm accessions, highlighting polygenic resistance and valuable introgression resources.

bioRxiv · Oct 4, 2026 · PreprintPlant Biology

Multi-trait modeling and machine learning discover new markers associated with stem traits in alfalfa

Cesar Augusto Medina, Deborah Jo Heuschele, Dongyan Zhao, Meng Lin, Craig Thomas Beil, Moira J. Sheehan, Zhanyou Xu

This paper identifies molecular markers associated with stem traits in alfalfa using GWAS and machine learning, finding thirteen significant markers linked to traits like stem color and winter injury.

Frontiers in Plant Science · Sep 9, 2024Peer-reviewedGenomics

Algorithmic and data modeling: will algorithmic modeling improve predictions of traits evaluated on ordinal scales?

Zhanyou Xu, Andreomar José Kurek, Steven B. Cannon, William Dale Beavis

This study compares genomic prediction methods for ordinal traits like IDC tolerance, finding that Random Forest provides the best decision metrics for breeding decisions.

bioRxiv · Oct 7, 2020 · PreprintGenomics