Author

Z. J. Winn

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Plant Biology· 3

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 2 Organism:plant (other)· 2 Approach:statistical model· 2 Assay:whole-genome sequencing· 2 Research Focus:gene regulatory network inference· 1 Research Focus:genome assembly· 1 Approach:machine learning (classical)· 1 Research Focus:variant effect prediction· 1

Frequent Co-Authors

E. Mason· 3 shared J. Lyerly· 2 shared N. D. DeWitt· 2 shared S. D. Haley· 2 shared A. Amsberry· 1 shared E. Hudson-Arns· 1 shared G. Bai· 1 shared G. J. Brown-Guedira· 1 shared

Activity by Year

2023-2023 · peak 3/yr

Co-Author Collaboration Network

10 top collaborators · 29 mutual co-author links

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3 shared papersPlant Biology

E. Mason

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Author profile →

3 papers

Recent·Most cited

HaploCatcher: An R Package for Prediction of Haplotypes

Z. J. Winn, E. Hudson-Arns, M. Hammers, N. D. DeWitt, J. Lyerly, G. Bai, P. St Amand, P. Nachappa, S. D. Haley, E. Mason

HaploCatcher predicts haplotypes using a random forest model trained on wheat lines with the Sst1 locus, achieving high accuracy in predicting stem solidness resistance.

The Plant Genome · Nov 15, 2023Peer-reviewedPlant Biology

Phenomic vs Genomic Prediction - A Comparison of Prediction Accuracies for Grain Yield in Hard Winter Wheat Lines

Z. J. Winn, A. Amsberry, S. D. Haley, N. D. DeWitt, E. Mason

This study compares genomic and phenomic prediction accuracies for grain yield in wheat, finding that phenomic estimates from spectral indices outperform genomic estimates from gBLUP models.

bioRxiv · Jul 19, 2023Plant Biology

UTILIZATION OF A PUBLICLY AVAILABLE DIVERSITY PANEL IN GENOMIC PREDICTION OF FUSARIUM HEAD BLIGHT RESISTANCE TRAITS IN WHEAT

Z. J. Winn, J. Lyerly, G. J. Brown-Guedira, J. P. Murphy, E. Mason

The paper uses a historical wheat dataset to evaluate genomic prediction of Fusarium head blight resistance traits, finding that environment clustering improves predictive accuracy.

bioRxiv · Feb 15, 2023 · PreprintPlant Biology