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An OMICs-based meta-analysis to support infection state stratification.

Ashleigh C Myall1,2, Simon Perkins1, David Rushton3

  • 1Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L697ZB, UK.

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|February 9, 2021
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Summary

This study developed a machine learning model to distinguish bacterial from viral infections using gene expression data. The model accurately predicts infection types, aiding in appropriate antibiotic use and reducing resistance.

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Area of Science:

  • Computational biology
  • Infectious disease diagnostics
  • Genomics

Background:

  • Antibiotics are ineffective against viral infections, leading to misuse when differentiating bacterial and viral illnesses.
  • Similar symptoms and lack of rapid diagnostics contribute to antibiotic overuse and resistance.
  • Measuring host response can differentiate infection states for optimal treatment.

Purpose of the Study:

  • To develop a predictive biomarker panel for distinguishing bacterial, viral, and no-infection states.
  • To utilize machine learning on human blood infection studies for diagnostic purposes.

Main Methods:

  • Conducted a meta-analysis of human blood infection studies using publicly available gene expression data.
  • Focused on Affymetrix and Illumina microarray data.
  • Developed multi-class machine learning models to predict infection states.

Main Results:

  • Achieved high accuracies in predicting infection types: 93% for bacterial and 89% for viral samples.
  • Reverse-engineered molecular regulatory networks to compare selected features across technologies.
  • Identified convergence in pathways such as Type I interferon Signaling, Chemotaxis, Apoptotic Processes, and Inflammatory/Innate Response.

Conclusions:

  • The developed models can accurately distinguish between bacterial and viral infections.
  • Despite technological differences, models converge on key biological pathways.
  • This approach supports precise diagnostics and judicious antibiotic prescribing.