A Comprehensive Machine Learning Approach for COVID-19 Target Discovery in the Small-Molecule Metabolome.

ABSTRACT: Background/Objectives: Respiratory viruses, including Influenza, RSV, and COVID-19, cause various respiratory infections. Distinguishing these viruses relies on diagnostic methods such as PCR testing. Challenges stem from overlapping symptoms and the emergence of new strains. Advanced diagnostics are crucial for accurate detection and effective management. This study leveraged nasopharyngeal metabolome data to predict respiratory virus scenarios including control vs. RSV, control vs. Influenza A, control vs. COVID-19, control vs. all respiratory viruses, and COVID-19 vs. Influenza A/RSV. Method: We proposed a stacking-based ensemble technique, integrating the top three best-performing ML models from the initial results to enhance prediction accuracy by leveraging the strength

SUBMITTER: Sumon MSI 

PROVIDER: S-EPMC11767724 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature