Addie Thompson

Drone imagery from 356 flights in 19 Genomes to Fields maize environments; vegetation-index functional principal components complement genomic kernels, predicting yield of held-out hybrids at r up to 0.501.

This paper introduces an environment-aware DNA language model to prioritize genomic regions associated with stress response in maize using stress-specific prompts and transformer-based modeling.

The paper demonstrates that integrating transcriptomic and phenomic data with genotypes improves trait prediction in maize across environments.

The paper investigates how host genotype and environment interact to shape bacterial endophyte communities in maize stalks, finding that gene-by-environment interactions significantly influence microbial communities.

This paper introduces distributional data analysis to plant phenomics, showing it captures novel heritable features in cotton and maize imagery that improve yield prediction and explain genetic variation.

This paper analyzes ancient Bolivian maize genomes to show genetic affinities with ancient Peruvian maize, suggesting targeted breeding strategies for improved growth by the 15th century.

This study identifies genetic loci and haplotypes associated with plant architectural and biomass yield traits in sorghum using genome-wide association studies and network analysis.

This study develops SSPA to prioritize SNPs associated with canopy height and growth rate in sorghum using GWAS and feature engineering.

PyBrOpS is a Python package that simulates and optimizes multi-objective breeding strategies, enabling the visualization of Pareto frontiers for decision support.

The paper assesses the sufficiency of genetic diversity in sorghum germplasm repositories for climate adaptation by evaluating geographic distribution and genomic resilience scores.

The paper predicts grain yield using non-yield traits and shows that a trait-based model performs comparably to genomic prediction models in cross-environment settings.