Extracting a COVID-19 Signature from a Multi-Omic Dataset

Baptiste Bauvin^1*Thibaud Godon^1*Guillaume Bachelot¹Claudia Carpentier¹Riikka Huusari²Maxime Deraspe¹Juho Rousu²Caroline Quach³Jacques Corbeil¹

• ¹Laval University, Quebec, Canada
• ²Aalto-yliopisto, Aalto, Finland
• ³Universite de Montreal, Montreal, Canada

We present a multi-omic signature for COVID-19 developed through a comprehensive Quebec initiative that established an extensive dataset of COVID-19 positive and negative patient samples.Moving beyond traditional symptomatic studies that rely on limited descriptors, our research integrates clinical, proteomic, and metabolomic data to classify COVID-19 status using thousands of features. Our multi-view machine learning approach extracts distinctive COVID-19 signatures from multi-omic data with remarkable effectiveness. By applying ensemble methods, we developed accurate and interpretable models for high-dimensional data-containing significantly more features than samples-achieving 89% ± 5% balanced accuracy. Through our novel feature relevance methodology, we identified condensed 12-and 50-feature signatures that enhanced classification accuracy by at least 3% compared to the original feature set. This approach successfully extracted and interpreted a robust multi-omic signature characterizing COVID-19positive individuals from a large, complex dataset, representing a significant advancement in COVID-19 biomarker discovery.