Moreland, Z. (AM) – Transcriptomic and Computational Analysis of Burn and Excisional Wound Healing

Accurate assessment of wound healing progress is critical for optimizing patient care and preventing complications, yet clinicians currently lack precise tools to determine where a wound stands in the healing timeline. Wound healing progresses through overlapping stages of inflammation, proliferation, and maturation, each marked by characteristic shifts in gene expression that are difficult to interpret without robust computational methods. This paper proposes to classify wound healing stages from transcriptomic data using support vector machines combined with biologically informed clustering to serve as features for the hierarchical SVM classifiers. This approach is applied to two distinct wound types: excisional wounds in pigs (21-day timeline) and burn wounds in mice (42-day timeline), enabling comparison of classification performance across different injury mechanisms. The models achieved high overall accuracy, with the burn model performing better at the classification of the stages. Both models made mistakes in distinguishing inflammation from early proliferation, highlighting the inherent biological overlap between these transitional healing stages. Overall, we find that transcriptomic-based classification can reliably identify wound healing stages across different wound types, providing a foundation for developing personalized diagnostic tools that could transform clinical wound management and improve patient outcomes.

Event Host: Zoe Moreland, M.S. Candidate, Applied Mathematics

Advisor: Marcella Gomez