biostudies-literatureUnknown5Bauvin B<h4>Introduction</h4>The complexity of COVID-19 requires approaches that extend beyond symptom-based descriptors. Multi-omic data, combining clinical, proteomic, and metabolomic information, offer a more detailed view of disease mechanisms and biomarker discovery.<h4>Methods</h4>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.<h4>Results</h4>Our models achieved a balanced accuracy of 89% ± 5% despite the high-dimensional nature of the dataFrontiers in bioinformatics1645785https://www.ebi.ac.uk/biostudies/studies/S-EPMC12497780biostudies-literatureExtracting a COVID-19 signature from a multi-omic dataset.PMC12497780Quach CCarpentier CHuusaari RBauvin BGodon TRousu JCorbeil JDeraspe MBachelot GfalseExtracting a COVID-19 signature from a multi-omic dataset.<h4>Introduction</h4>The complexity of COVID-19 requires approaches that extend beyond symptom-based descriptors. Multi-omic data, combining clinical, proteomic, and metabolomic information, offer a more detailed view of disease mechanisms and biomarker discovery.<h4>Methods</h4>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.<h4>Results</h4>Our models achieved a balanced accuracy of 89% ± 5% despite the high-dimensional nature of the data2025-01-01T00:00:00Z20252026-06-04T06:56:33.59Z2026-05-06T03:11:55.08ZS-EPMC124977804105948410.3389/fbinf.2025.1645785