Guan-Shuo Mai

MorphQ provides a label-free method to quantify and visualize complex morphology from standardized images, outperforming PCA and supervised models in downstream biological analysis.

The paper uses circadian activity data and machine learning to predict breeding phenology in Nicrophorus nepalensis across different elevations and photoperiods, achieving high accuracy.