Portrait de Jacques Corbeil

Jacques Corbeil

Membre affilié
Professeur titulaire, Université Laval
Linearis Venture and Labs
Sujets de recherche
Apprentissage automatique appliqué
Biologie computationnelle
IA et santé
Site web
Google Scholar
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Biographie

Le Dr Jacques Corbeil se concentre sur l'utilisation des dernières techniques de bio-informatique et d'apprentissage automatique pour faciliter le diagnostic, le pronostic et la réponse au traitement. Les techniques modernes de génomique et de métabolomique génèrent un déluge de données qui doivent être interprétées à l'aide de nouvelles approches informatiques. Le Dr Corbeil utilise des instruments et des méthodologies de pointe pour faciliter l'interprétation de données complexes, y compris la spectrométrie de masse à haut débit, en particulier la métabolomique et le séquençage next-gen.

Les recherches du Dr Corbeil portent notamment sur la manière dont les micro-organismes infectieux interagissent avec leur hôte, sur les effets des antibiotiques sur notre flore microbienne et sur l'environnement, et sur la manière de concevoir de petites molécules et des médicaments pour interférer avec des fonctions microbiennes spécifiques et avec la progression du cancer. Travaillant à l'interface de l'apprentissage automatique et des sciences omiques, il possède une expertise dans l'intégration des données omiques et se spécialise dans l'analyse de données massives appliquée aux maladies infectieuses et au cancer. Le Dr Corbeil collabore avec de nombreuses industries pour améliorer leurs processus et mettre en œuvre des stratégies d'intelligence artificielle. Depuis 2004, le Dr Corbeil est titulaire de la Chaire de recherche du Canada en génomique médicale (niveau 1).

Publications

Invariant Causal Set Covering Machine
Thibaud Godon
Baptiste Bauvin
Pascal Germain
Jacques Corbeil
Alexandre Drouin
Rule-based models, such as decision trees, appeal to practitioners due to their interpretable nature. However, the learning algorithms that … (voir plus)produce such models are often vulnerable to spurious associations, and thus, they are not guaranteed to extract causally relevant insights. This limitation reduces their utility in gaining mechanistic insights into a phenomenon of interest. In this work, we build on ideas from the invariant causal prediction literature to propose Invariant Causal Set Covering Machines, an extension of the classical Set Covering Machine (SCM) algorithm for conjunctions/disjunctions of binary-valued rules that provably avoids spurious associations. The proposed method leverages structural assumptions about the functional form of such models, enabling an algorithm that identifies the causal parents of a variable of interest in polynomial time. We demonstrate the validity and efficiency of our approach through a simulation study and highlight its favorable performance compared to SCM in uncovering causal variables across real-world datasets.
2026-08-09
Transactions on Machine Learning Research (accepté)
openreview.net
openreview.net
Refnd: Preventing Data Leakage in Relational Datasets
Anthony Lavertu
Jacob Côté
Jacques Corbeil
Sophie Gobeil
Pascal Germain
Machine learning models trained on biochemical data are routinely evaluated using splits that fail to account for relational structure, caus… (voir plus)ing information leakage and over-optimistic performance estimates. Existing splitting methods lack theoretical grounding and scale at best quadratically. We introduce the Relational Generative Process (RGP), a mathematical formalization explaining why relational structure arises in biochemical datasets, and Refnd, a splitting algorithm that leverages a proximity graph computed in loglinear time using Hierarchical Navigable Small World (HNSW). We validate on an antimicrobial peptide dataset, showing that Refnd splits yield lower but more realistic evaluation performance than traditional splits. Refnd is applicable to any dataset arising from an RGP such as protein sequences and structures, small molecules, and nucleotide sequences, and is openly available as a Rust accelerated Python package: pip install refnd.
2026-06-29
arXiv (prépublication)
doi.org
arxiv.org
QMAP: A Benchmark for Standardized Evaluation of Antimicrobial Peptide MIC and Hemolytic Activity Regression
Anthony Lavertu
Jacques Corbeil
Pascal Germain
Antimicrobial peptides (AMPs) are promising alternatives to conventional antibiotics, but progress in computational AMP discovery has been d… (voir plus)ifficult to quantify due to inconsistent datasets and evaluation protocols. We introduce QMAP, a domain-specific benchmark for predicting AMP antimicrobial potency (MIC) and hemolytic toxicity (HC50) with homology-aware, predefined test sets. QMAP enforces strict sequence homology constraints between training and test data, ensuring that model performance reflects true generalization rather than overfitting. Applying QMAP, we reassess existing MIC models and establish baselines for MIC and HC50 regression. Results show limited progress over six years, poor performance for high-potency MIC regression, and low predictability for hemolytic activity, emphasizing the need for standardized evaluation and improved modeling approaches for highly potent peptides. We release a Python package facilitating practical adoption, and with a Rust-accelerated engine enabling efficient data manipulation, installable with pip install qmap-benchmark.
2026-02-03
bioRxiv (prépublication)
doi.org
Extracting a COVID-19 signature from a multi-omic dataset
Baptiste Bauvin
Thibaud Godon
Guillaume Bachelot
Claudia Carpentier
Riikka Huusaari
Maxime Déraspe
Juho Rousu
Caroline Quach
Jacques Corbeil
The complexity of COVID-19 requires approaches that extend beyond symptom-based descriptors. Multi-omic data, combining clinical, proteomic,… (voir plus) and metabolomic information, offer a more detailed view of disease mechanisms and biomarker discovery. As part of a large-scale Quebec initiative, we collected extensive datasets from COVID-19 positive and negative patient samples. Using a multi-view machine learning framework with ensemble methods, we integrated thousands of features across clinical, proteomic, and metabolomic domains to classify COVID-19 status. We further applied a novel feature relevance methodology to identify condensed signatures. Our models achieved a balanced accuracy of 89% ± 5% despite the high-dimensional nature of the data. Feature selection yielded 12- and 50-feature signatures that improved classification accuracy by at least 3% compared to the full feature set. These signatures were both accurate and interpretable. This work demonstrates that multi-omic integration, combined with advanced machine learning, enables the extraction of robust COVID-19 signatures from complex datasets. The condensed biomarker sets provide a practical path toward improved diagnosis and precision medicine, representing a significant advancement in COVID-19 biomarker discovery.
2025-09-21
Frontiers in Bioinformatics (publié)
doi.org