Machine learning and precision medicine to curb atherosclerosis

Principal Investigator: Jean-Claude Tardif
Theme : Health
Competition : 2017 Competition: IVADO's Grants for fundamental research projects
Status : Completed
Start : Apr. 1, 2019
End: Mar. 31, 2020
Budget : $270,000.00


Cardiovascular disease (CVD) remains the leading cause of death worldwide, yet less than half of patients benefit from preventive CVD medications. Drug responses are influenced by patients' genomic profiles, other individual characteristics, and environmental factors. Until recently, these factors were generally not taken into account in clinical studies, leading to an evaluation of treatments that underestimated their usefulness or failed to detect deleterious effects in certain patient groups. The main objective of this project is to use precision medicine and machine learning techniques to automatically discover individual characteristics that are predictive of disease development or progression and responses to multiple CVD drugs (efficacy and toxicity). The team proposes to integrate multiple data (genetic, multi-omics, imaging, clinical data) and biomarkers using traditional statistical methods and novel machine learning approaches. Their approach will enable the identification and validation of drug targets, and will lead to both the transformation of CVD drug development as well as the improvement of patient care (especially for atherosclerosis-related diseases.


Lead Genome Centre: Génome Québec


Partner: IVADO




Marie-Pierre Dubé Université de Montréal
Julie Hussin Université de Montréal
Joëlle Pineau McGill