Franck Giacomoni

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

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.

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

WiPP improves GC-MS peak detection by combining multi-algorithm results with machine learning-based quality assessment, enhancing accuracy and enabling algorithm comparison.