Extracting a COVID-19 signature from a multi-omic dataset
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.
Authors
- Jacques Corbeil (ORCID: https://orcid.org/0000-0002-9973-2740)
- Maxime Déraspe (ORCID: https://orcid.org/0000-0002-9094-0500)
- Claudia Carpentier
- Baptiste Bauvin (ORCID: https://orcid.org/0000-0002-7985-058X)
- Thibaud Godon (ORCID: https://orcid.org/0000-0003-2813-3668)
- Caroline Quach (ORCID: https://orcid.org/0000-0002-1170-9475)
- Guillaume Bachelot (ORCID: https://orcid.org/0000-0002-4175-4816)
- Juho Rousu (ORCID: https://orcid.org/0000-0002-0705-4314)
- Riikka Huusaari
Institutions
- Engineering Link (Canada) (CA)
- Mila - Quebec Artificial Intelligence Institute (CA)
- Université Laval (CA)
- Université de Montréal (CA)
- Aalto University (FI)
Publication Details
- Journal
- Frontiers in Bioinformatics
- Published
- 2025-09-22
- DOI
- https://doi.org/10.3389/fbinf.2025.1645785
- Citations
- 1
- Primary Topic
- Bioinformatics and Genomic Networks
- Type
- article
- Field-Weighted Citation Impact
- 0.81
Funders
- Public Health Agency of Canada
- Génome Québec
- Ministère de la Santé et des Services sociaux
- Public Health Agency
- Ministère de la Santé
- Canadian Institutes of Health Research