Isoform Markers of Brain Aging
Using transcript isoform patterns to study brain aging and compare markers across brain regions.
Transcript usage may reveal aging-related patterns that total gene expression misses. In the Hicks Lab, I developed isoform-level machine-learning workflows for bulk RNA-seq to study age-associated markers in human brain tissue. The analysis represents transcript usage through isoform fractions and combines feature selection with age-prediction models.
Cross-region evaluation examines whether markers transfer between brain regions or reflect hippocampus-specific patterns. My work included feature construction, model evaluation, comparison of shared and region-specific markers, and visualization of the results. This project provides a biological setting for studying interpretable transcript-level variation across the lifespan and was presented as a conference poster at Biology of Genomes, Cold Spring Harbor Laboratory, in 2026.
