Nathalie Poupin

This study simulates metabolic profiles to evaluate pathway analysis methods, revealing biases when applied to metabolomics data.

PathIntegrate integrates multi-omics data using pathway-based modeling to identify important pathways and molecules contributing to outcomes, demonstrated on COPD and COVID-19 datasets.

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.

This paper introduces a strategy to detect metabolic changes from chemical exposure using transcriptomics data and metabolic models, identifying potential mechanisms of action in vitro.

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

This paper introduces DEXOM, a method to enumerate diverse optimal metabolic networks from multi-omics data to improve in-silico gene essentiality predictions in yeast.