Florence Vinson

The paper introduces SAMBA, a computational method that predicts metabolite changes under metabolic perturbations using genome-scale metabolic network modeling and compares predictions with experimental data.

The paper identifies disease associations for 1,047 overlooked metabolites by extending the guilt by association principle using a Bayesian framework, resulting in 35,000 new associations.

This paper evaluates over-representation analysis (ORA) in metabolomics, showing how different parameters and datasets affect results and offering best practices for its use.

The paper introduces FORUM, a Knowledge Graph that extracts and connects chemical-disease associations from databases and literature to support biological interpretation.