Joshua Colmer

This study develops ChronoGauge, a machine learning model that estimates plant circadian time from transcriptomic data, enabling analysis of environmental and genetic factors affecting the circadian clock.

This study annotates the wheat pan-transcriptome de novo and identifies gene diversity and expression patterns across multiple wheat cultivars and tissues.

This paper uses machine learning to predict circadian gene expression in Arabidopsis using DNA sequence features, identifies key regulatory elements, and optimizes sampling strategies for circadian transcript prediction.

This paper introduces a backpack LiDAR and GUI-based software solution for large-scale wheat phenotyping, showing it can accurately measure crop traits and respond to nitrogen treatments.