Tackling metabolic diseases using high throughput mass spectrometry coupled to artificial intelligence algorithms

This project will provide an artificial intelligence-based tool to predict biomarkers associated to metabolomic imbalances in multiple cell types and disease states. Thanks to the large amount of mass spectrometry data on which state-of-the-art machine learning algorithms will be trained, the software solution will achieve high accuracy, clinical-grade, predictions. Ultimately, the software will provide possible targets for small molecule therapy. This is particularly important to investigate since it will reduce the pharmaceutical and biotechnological companies’ need for a trial and error approach, saving time and money on R&D programs. Ultimately, patients will benefit from safer and more efficient treatment thanks to our approach.

Intern: 
Elsa Rousseau
Superviseur universitaire: 
Jacques Corbeil
Province: 
Quebec
Université: 
Partenaire: 
Partner University: