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

Md Shaheenur Islam Sumon¹, Md Sakib Abrar Hossain², Haya Al-Sulaiti^3,4

¹Department of Electrical Engineering, Qatar University, Doha P.O. Box 2713, Qatar.

|January 24, 2025

Summary

This summary is machine-generated.

Nasopharyngeal metabolomics combined with machine learning accurately predicts respiratory viruses like COVID-19, Influenza, and RSV. This approach identifies key metabolites for improved diagnostics and management of respiratory infections.

Keywords:

COVID-19 diagnostic markers machine learning metabolomics respiratory viruses

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Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry UPLC-MS

Published on: March 14, 2013

Area of Science:

Biochemistry
Computational Biology
Infectious Disease Diagnostics

Background:

Respiratory viruses (Influenza, RSV, COVID-19) present diagnostic challenges due to overlapping symptoms and evolving strains.
Accurate and timely diagnosis is critical for effective management of respiratory infections.
Current diagnostic methods like PCR testing face limitations in speed and scope.

Purpose of the Study:

To predict respiratory virus infection scenarios using nasopharyngeal metabolome data.
To develop an advanced diagnostic framework leveraging machine learning and metabolomics.
To identify key metabolic biomarkers for differentiating respiratory viruses.

Main Methods:

Utilized a stacking-based ensemble machine learning technique integrating top-performing models.
Employed feature ranking, standard scaling, and SMOTE to enhance model robustness and address class imbalance.
Applied SHAP analysis to identify significant metabolites influencing viral predictions.

Main Results:

The ensemble model demonstrated superior performance in predicting respiratory virus scenarios.
Identified key metabolites for COVID-19 prediction, including Lysophosphatidylcholine acyl C18:2, Kynurenine, Phenylalanine, Valine, Tyrosine, and Aspartic Acid.
The approach successfully differentiated between control, individual viruses, and combinations.

Conclusions:

Nasopharyngeal metabolome data is effective for predicting respiratory virus infections.
Stacking-based ensemble techniques significantly enhance prediction accuracy.
The study provides a robust framework and identifies potential biomarkers for respiratory virus diagnostics.