Jacqueline C. Bresnahan

This paper develops an AutoML framework with explainable AI tools to identify hemodynamic predictors of spinal cord injury outcomes.

The paper introduces ODC-TBI, a platform for sharing preclinical TBI data, enabling big data analytics and machine learning to address heterogeneity in TBI research.

The paper describes the development and implementation of a vocabulary extension pipeline for the SPARC program to improve FAIR data practices in autonomic nervous system research.

The paper identifies soluble TNF receptor 1 (sTNFR1) as a therapeutic target for spinal cord injury using multi-dimensional analysis of preclinical data.

The paper identifies 197 genes in peripheral blood cells whose expression correlates with acute spinal cord injury severity, achieving 72.7% accuracy in predicting injury grades.