An OMICs-based meta-analysis to support infection state stratification
Abstract
A fundamental problem for disease treatment is that while antibiotics are a powerful counter to bacteria, they are ineffective against viruses. Often, bacterial and viral infections are confused due to their similar symptoms and lack of rapid diagnostics. With many clinicians relying primarily on symptoms for diagnosis, overuse and misuse of modern antibiotics are rife, contributing to the growing pool of antibiotic resistance. To ensure an individual receives optimal treatment given their disease state and to reduce over-prescription of antibiotics, the host response can in theory be measured quickly to distinguish between the two states. To establish a predictive biomarker panel of disease state (viral/bacterial/no-infection), we conducted a meta-analysis of human blood infection studies using machine learning.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Feb 09, 2021
- Source ID
- 10.1093/bioinformatics/btab089
Entities
People
- Andrew R Jones
- Ashleigh C Myall
- David Rushton
- Jonathan David
- Philipp Antczak
- Phillippa Spencer
- Simon Perkins
Organizations
- Defence Science and Technology Laboratory
- Defense Threat Reduction Agency
- Imperial College London
- University of Cologne
- University of Liverpool