| Title: |
Extracting a COVID-19 signature from a multi-omic dataset |
| Authors: |
Bauvin, Baptiste; Godon, Thibaud; Bachelot, Guillaume; Carpentier, Claudia; Huusaari, Riikka; Deraspe, Maxime; Rousu, Juho; Quach, Caroline; Corbeil, Jacques |
| Contributors: |
Department of Computer Science; Professorship Rousu Juho; Computer Science Professors; Computer Science - Large-scale Computing and Data Analysis (LSCA) - Research area; Computer Science - Artificial Intelligence and Machine Learning (AIML) - Research area; Computer Science - Computational Life Sciences (CSLife) - Research area; Helsinki Institute for Information Technology (HIIT); Université Laval; Université de Montréal; Aalto-yliopisto; Aalto University |
| Publisher Information: |
Frontiers Media |
| Publication Year: |
2025 |
| Collection: |
Aalto University Publication Archive (Aaltodoc) / Aalto-yliopiston julkaisuarkistoa |
| Subject Terms: |
biomarker; COVID-19; machine learning; metabolomics; multi-omics; proteomics; signature |
| Description: |
Publisher Copyright: Copyright © 2025 Bauvin, Godon, Bachelot, Carpentier, Huusaari, Deraspe, Rousu, Quach and Corbeil. ; Introduction: 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. Methods: 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. Results: 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. Discussion: 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. ; Peer reviewed |
| Document Type: |
article in journal/newspaper |
| File Description: |
application/pdf |
| Language: |
English |
| Relation: |
Frontiers in Bioinformatics; Volume 5, pp. 1-13; https://aaltodoc.aalto.fi/handle/123456789/140436 |
| DOI: |
10.3389/fbinf.2025.1645785 |
| Availability: |
https://aaltodoc.aalto.fi/handle/123456789/140436; https://doi.org/10.3389/fbinf.2025.1645785 |
| Rights: |
openAccess ; CC BY ; https://creativecommons.org/licenses/by/4.0/ |
| Accession Number: |
edsbas.4A68A864 |
| Database: |
BASE |