Portrait of Jacques Corbeil

Jacques Corbeil

Affiliate Member
Full Professor, Université Laval
General Partner et CSO, Linearis Venture and Labs
Research Topics
AI and Healthcare
Applied Machine Learning
Computational Biology
Website
Google Scholar
LinkedIn

Biography

Dr. Jacques Corbeil focuses on using the latest techniques in bioinformatics and machine learning to assist diagnostic, prognostic and response to treatment. Modern genomics and metabolomics techniques generate a deluge of data that needs to be interpreted using novel informatics approaches. Dr. Corbeil uses state-of-the-art instrumentation and methodologies to facilitate the interpretation of complex data, including high throughput mass spectrometry, in particular, metabolomics and next-gen sequencing.

Dr. Corbeil’s research includes investigating how infectious microorganisms interact with their host, the effects of antibiotics on our microbial flora and the environment, and exploring how to design small molecules and drugs to interfere with specific microbial functions and cancer progression. Operating at the interface of machine learning and omics sciences, he specializes in big data analytics applied to infectious diseases and cancer and he has expertise in the integration of omics data. Dr. Corbeil collaborates with many industries to ameliorate their process and implement artificial intelligence strategies. Since 2004, Dr. Corbeil holds the Canada Research Chair in Medical Genomics (Tier 1).

Publications

Associations between circulating amino acids and metabolic dysfunction‐associated steatotic liver disease in individuals living with severe obesity
Ina Maltais‐Payette
Jérôme Bourgault
Marie‐Frédérique Gauthier
Laurent Biertho
Simon Marceau
François Julien
Patricia L. Mitchell
Christian Couture
Francis Brière
Jacques Corbeil
Benoit J. Arsenault
André Tchernof
2025-01-27
Physiological Reports (published)
doi.org
Turncoat antibodies unmasked in a model of autoimmune demyelination: from biology to therapy
Reza Taghipour-Mirakmahaleh
Françoise Morin
Yu Zhang
Louis Bourhoven
Louis-Charles Béland
Qun Zhou
Julie Jaworski
Anna Park
Juan Manuel Dominguez
Jacques Corbeil
Eoin P Flanagan
Romain Marignier
Catherine Larochelle
Steven Kerfoot
Luc Vallières
Autoantibodies contribute to many autoimmune diseases, yet there is no approved therapy to neutralize them selectively. A popular mouse mode… (see more)l, experimental autoimmune encephalomyelitis (EAE), could serve to develop such a therapy, provided we can better understand the nature and importance of the autoantibodies involved. Here we report the discovery of autoantibody-secreting extrafollicular plasmablasts in EAE induced with specific myelin oligodendrocyte glycoprotein (MOG) antigens. Single-cell RNA sequencing reveals that these cells produce non-affinity-matured IgG antibodies. These include pathogenic antibodies competing for shared binding space on MOG’s extracellular domain. Interestingly, the synthetic anti-MOG antibody 8-18C5 can prevent the binding of pathogenic antibodies from either EAE mice or people with MOG antibody disease (MOGAD). Moreover, an 8-18C5 variant carrying the NNAS mutation, which inactivates its effector functions, can reduce EAE severity and promote functional recovery. In brief, this study provides not only a comprehensive characterization of the humoral response in EAE models, but also a proof of concept for a novel therapy to antagonize pathogenic anti-MOG antibodies.
2024-12-06
bioRxiv (preprint)
doi.org
Invariant Causal Set Covering Machines
Thibaud Godon
Baptiste Bauvin
Pascal Germain
Jacques Corbeil
Alexandre Drouin
2023-06-07
ArXiv (preprint)
doi.org
arxiv.org
MOT: A Multi-Omics Transformer for Multiclass Classification Tumour Types Predictions
Mazid Osseni
Prudencio Tossou
Franccois Laviolette
Jacques Corbeil
2023-02-16
Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (published)
doi.org
Sample Boosting Algorithm (SamBA) - An interpretable greedy ensemble classifier based on local expertise for fat data
Baptiste Bauvin
Cécile Capponi
Florence Clerc
Pascal Germain
Sokol Koço
Jacques Corbeil
2023-01-01
UAI (published)
proceedings.mlr.press
openreview.net
RandomSCM: interpretable ensembles of sparse classifiers tailored for omics data
Thibaud Godon
Pier-Luc Plante
Baptiste Bauvin
Élina Francovic-Fontaine
Alexandre Drouin
Jacques Corbeil
Background: Understanding the relationship between the Omics and the phenotype is a central problem in precision medicine. The high dimensio… (see more)nality of metabolomics data challenges learning algorithms in terms of scalability and generalization. Most learning algorithms do not produce interpretable models -- Method: We propose an ensemble learning algorithm based on conjunctions or disjunctions of decision rules. -- Results : Applications on metabolomics data shows that it produces models that achieves high predictive performances. The interpretability of the models makes them useful for biomarker discovery and patterns discovery in high dimensional data.
2022-08-11
ArXiv (preprint)
doi.org
arxiv.org