Úna Chan

This paper uses machine learning to identify cellular signatures in human motor neurons that distinguish TDP-43 ALS subtypes and enable patient stratification.

This study identifies opposing roles of p38α phosphorylation and arginine methylation in TDP-43 proteinopathy, showing that phosphorylation promotes aggregation while methylation reduces it.

The paper constructs a PPI network of 100 high-confidence ASD genes, identifies novel interactions, and uses AlphaFold-Multimer to prioritize variants for functional validation in organoids.