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Thibaud Godon

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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
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
On Selecting Robust Approaches for Learning Predictive Biomarkers in Metabolomics Data Sets.
Thibaud Godon
Pier-Luc Plante
J. Corbeil
Pascal Germain
Alexandre Drouin
Metabolomics, the study of small molecules within biological systems, offers insights into metabolic processes and, consequently, holds grea… (voir plus)t promise for advancing health outcomes. Biomarker discovery in metabolomics represents a significant challenge, notably due to the high dimensionality of the data. Recent work has addressed this problem by analyzing the most important variables in machine learning models. Unfortunately, this approach relies on prior hypotheses about the structure of the data and may overlook simple patterns. To assess the true usefulness of machine learning methods, we evaluate them on a collection of 835 metabolomics data sets. This effort provides valuable insights for metabolomics researchers regarding where and when to use machine learning. It also establishes a benchmark for the evaluation of future methods. Nonetheless, the results emphasize the high diversity of data sets in metabolomics and the complexity of finding biologically relevant biomarkers. As a result, we propose a novel approach applicable across all data sets, offering guidance for future analyses. This method involves directly comparing univariate and multivariate models. We demonstrate through selected examples how this approach can guide data analysis across diverse data set structures, representative of the observed variability. Code and data are available for research purposes.
2025-06-11
Analytical Chemistry (publié)
doi.org
Invariant Causal Set Covering Machines
Thibaud Godon
Baptiste Bauvin
Pascal Germain
J. Corbeil
Alexandre Drouin
2023-06-06
ArXiv (prépublication)
doi.org
arxiv.org
RandomSCM: interpretable ensembles of sparse classifiers tailored for omics data
Thibaud Godon
Pier-Luc Plante
Baptiste Bauvin
Élina Francovic-Fontaine
Alexandre Drouin
J. Corbeil
Background: Understanding the relationship between the Omics and the phenotype is a central problem in precision medicine. The high dimensio… (voir plus)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.
2022-08-10
ArXiv (prépublication)
doi.org
arxiv.org