Portrait de Marc-André Legault

Marc-André Legault

Membre académique associé
Professeur adjoint, Université de Montréal, Pharmacogénétique
Sujets de recherche
Apprentissage automatique médical
Biologie computationnelle
Causalité
Google Scholar

Biographie

Marc-André Legault a obtenu son doctorat en bioinformatique à l'Université de Montréal et à l'Institut de Cardiologie de Montréal où il a développé et appliqué de nouvelles méthodes computationnelles pour la validation de cibles médicamenteuses. Il a ensuite complété sa formation postdoctorale à l'Université McGill et à Mila - Institut québécois d'intelligence artificielle, travaillant sur l'estimation de variables instrumentales et l'apprentissage machine pour l'épidémiologie génétique en général.

Il est maintenant professeur adjoint en pharmacogénétique à la Faculté de pharmacie de l'Université de Montréal et chercheur au Centre de recherche Azrieli du CHU Sainte-Justine. Son programme de recherche vise à développer et à utiliser des approches computationnelles pour la validation de cibles médicamenteuses afin de mieux comprendre l'hétérogénéité des traitements et d'améliorer notre capacité à anticiper l'effet sur cible de nouvelles classes de médicaments. Il est également un membre académique associé à Mila - Institut québécois d'intelligence artificielle.

Étudiants actuels

Julien St-Pierre
Postdoctorat - UdeM
Github
Google Scholar

Publications

Do machine learning methods make better predictions than conventional ones in pharmacoepidemiology? A systematic review, meta-analysis, and network meta-analysis.
Ana Paula Bruno Pena-Gralle
Mireille E. Schnitzer
Sofia-Nada Boureguaa
Félix Morin
Marc-André Legault
Caroline Sirois
Alice Dragomir
Lucie Blais
2025-12-31
Artif. Intell. Medicine (publié)
doi.org
PheCode-guided multi-modal topic modeling of electronic health records improves disease incidence prediction and GWAS discovery from UK Biobank
Ziqi Yang
Ziyang Song
Shadi Zabad
Marc-André Legault
Yue Li
Phenome-wide association studies rely on disease definitions derived from diagnostic codes, often failing to leverage the full richness of e… (voir plus)lectronic health records (EHR). We present MixEHR-SAGE, a PheCode-guided multi-modal topic model that integrates diagnoses, procedures, and medications to enhance phenotyping from large-scale EHRs. By combining expert-informed priors with probabilistic inference, MixEHR-SAGE identifies over 1000 interpretable phenotype topics from UK Biobank data. Applied to 350 000 individuals with high-quality genetic data, MixEHR-SAGE-derived risk scores accurately predict incident type 2 diabetes (T2D) and leukemia diagnoses. Subsequent genome-wide association studies using these continuous risk scores uncovered novel disease-associated loci, including PPP1R15A for T2D and JMJD6/SRSF2 for leukemia, that were missed by traditional binary case definitions. These results highlight the potential of probabilistic phenotyping from multi-modal EHRs to improve genetic discovery. The MixEHR-SAGE software is publicly available at: https://github.com/li-lab-mcgill/MixEHR-SAGE.
2025-12-31
Briefings in Bioinformatics (publié)
doi.org
Reply to comment on "medication-based mortality prediction in COPD using machine learning and conventional statistical methods".
Ana Paula Pena-Gralle
Amélie Forget
Yohann Moanahere Chiu
Marc-André Legault
M. Beauchesne
Lucie Blais
2025-12-31
International Journal of Medical Informatics (publié)
doi.org
Medication-based mortality prediction in COPD using machine learning and conventional statistical methods.
Ana Paula Pena-Gralle
Amélie Forget
Yohann Chiu
Marc-André Legault
M. Beauchesne
Lucie Blais
2025-11-04
International Journal of Medical Informatics (publié)
doi.org
Genetic contribution to asthma informs acute chest syndrome pathophysiology and risk stratification
Sara El Aouhel
Vanessa Bellegarde
Stennio Da
Silva Faria
Tristan St-Laurent
Estelle Lecluze
Anne-Laure Pham Hung d’Alexandry d’Orengiani
F. Galactéros
Pablo Bartolucci
Marc-André Legault
Guillaume Lettre
Thomas Pincez
2025-10-02
medRxiv (prépublication)
doi.org
Genetic contribution to asthma informs acute chest syndrome pathophysiology and risk stratification
Sara El Aouhel
Vanessa Bellegarde
Stennio Da
Silva Faria
Tristan St-Laurent
Estelle Lecluze
Anne-Laure Pham Hung d’Alexandry d’Orengiani
F. Galactéros
Pablo Bartolucci
Marc-André Legault
Guillaume Lettre
Thomas Pincez
2025-10-02
medRxiv (prépublication)
doi.org
A flexible machine learning Mendelian randomization estimator applied to predict the safety and efficacy of sclerostin inhibition
Marc-André Legault
Jason Hartford
Benoît J. Arsenault
Archer Yang
Joelle Pineau
2025-05-01
American Journal of Human Genetics (publié)
doi.org
Association Between Circulating Vitamin K Levels, Gut Microbiome, and Type 1 Diabetes: A Mendelian Randomization Study
Samuel De La Barrera
Benjamin De La Barrera
Marc-André Legault
Isabel Gamache
Despoina Manousaki
2024-11-01
Nutrients (publié)
doi.org
Do machine learning methods Make Better predictions in pharmacoepidemiology?
Ana Paula Pena-Gralle
Mireille E. Schnitzer
Sofia-Nada Boureguaa
Félix Morin
Marc-André Legault
Caroline Sirois
Alice Dragomir
Lucie Blais
2024-09-01
Annals of Epidemiology (publié)
doi.org
Predicting Five-Year All-Cause Mortality in COPD Patients Using Machine Learning
Ana Paula Pena-Gralle
Amélie Forget
Sofia-Nada Boureguaa
Marc-André Legault
Lucie Blais
2024-09-01
Annals of Epidemiology (publié)
doi.org
A novel and efficient machine learning Mendelian randomization estimator applied to predict the safety and efficacy of sclerostin inhibition
Marc-André Legault
Jason Hartford
Benoît J. Arsenault
Y. Archer
Yang
Joelle Pineau
Mendelian Randomization (MR) enables estimation of causal effects while controlling for unmeasured confounding factors. However, traditional… (voir plus) MR's reliance on strong parametric assumptions can introduce bias if these are violated. We introduce a new machine learning MR estimator named Quantile Instrumental Variable (IV) that achieves low estimation error in a wide range of plausible MR scenarios. Quantile IV is distinctive in its ability to estimate nonlinear and heterogeneous causal effects and offers a flexible approach for subgroup analysis. Applying Quantile IV, we investigate the impact of circulating sclerostin levels on heel bone mineral density, osteoporosis, and cardiovascular outcomes in the UK Biobank. Employing various MR estimators and colocalization techniques that allow multiple causal variants, our analysis reveals that a genetically predicted reduction in sclerostin levels significantly increases heel bone mineral density and reduces the risk of osteoporosis, while showing no discernible effect on ischemic cardiovascular diseases. Quantile IV contributes to the advancement of MR methodology, and the case study on the impact of circulating sclerostin modulation contributes to our understanding of the on-target effects of sclerostin inhibition.
2024-01-31
medRxiv (prépublication)
doi.org
Deep interpretability for GWAS
Deepak Sharma
Audrey Durand
Marc-André Legault
Louis-philippe Lemieux Perreault
Audrey Lemaccon
Marie-Pierre Dub'e
Joelle Pineau
Genome-Wide Association Studies are typically conducted using linear models to find genetic variants associated with common diseases. In the… (voir plus)se studies, association testing is done on a variant-by-variant basis, possibly missing out on non-linear interaction effects between variants. Deep networks can be used to model these interactions, but they are difficult to train and interpret on large genetic datasets. We propose a method that uses the gradient based deep interpretability technique named DeepLIFT to show that known diabetes genetic risk factors can be identified using deep models along with possibly novel associations.
2020-07-03
ArXiv (prépublication)
arxiv.org
arxiv.org