Mehdi Babaei

This study characterizes volatile organic compounds (VOCs) in 165 cannabis cultivars, identifying three chemotypes based on VOC profiles and their odor activity.

This study performs a sex chromosome-aware GWAS on cannabis, identifying 16 genome-wide significant markers on ChrX and ChrY that influence agronomic traits.

This study uses machine learning to classify flowering time in Cannabis landraces by integrating genomic and morphophysiological data, achieving 96.6% accuracy.

This study identifies 91 genomic regions associated with 40 traits in Cannabis landraces using GWAS, revealing pleiotropic loci and highlighting their potential for genetic improvement.

This study compares sexual expression, biomass, cannabinoid content, and seed production in XXX and XXY triploid cannabis plants to understand Y chromosome role in sex determination.