Clément Frainay

The paper introduces Kg4j, a framework for building tailored knowledge graphs to explore disease-exposure relationships, applied to endometriosis and persistent pollutants.

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.

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.