Phenotyping, comparative biology, & machine learning

Linking photosynthetic anatomy, physiology, and genomic features across vascular plants.

Advances in sequencing have facilitated RNA and DNA data collection, but phenotyping — measuring any and all observable traits, such as morphology, physiology, or genomic features like gene copy number — remains a bottleneck for many areas of biology. During the course of various projects I have developed and applied novel methods to collect and analyze phenotypic data to understand the patterns and processes that shape vascular plant diversity.

Related publications: (Gilman et al., 2024) (Marks et al., 2024)

References

2024

  1. Predicting photosynthetic pathway from anatomy using machine learning
    I. S. Gilman, K. Heyduk, C. Maya-Lastra, and 2 more authors
    New Phytologist, 2024
  2. Convergent evolution of desiccation tolerance in grasses
    R. A. Marks, L. V. D. Pas, J. Schuster, and 2 more authors
    Nature Plants, 2024