Extracting a COVID-19 signature from a multi-omic dataset.

ABSTRACT:

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

SUBMITTER: Bauvin B 

PROVIDER: S-EPMC12497780 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature