Natasha Mulligan

This study shows that chemical foundation models can capture glycobiology principles, including immunogenicity and protein-glycan interactions, by fine-tuning atom-level representations from general chemistry.

This paper uses RNA foundation models to improve endometriosis classification across cohorts and identifies stable gene-level predictors.

GENET streamlines hypothesis generation for gene-disease associations by integrating AI-driven network analysis, literature mining, clustering, and visualization.

This paper develops an in vitro model of the human intestinal epithelium that closely mimics in vivo cell types using single-cell transcriptomics and a biomedical foundation model.