A Genome-Based Model to Predict the Virulence of <named-content content-type="genus-species">Pseudomonas aeruginosa</named-content> Isolates

A Genome-Based Model to Predict the Virulence of <named-content content-type="genus-species">Pseudomonas aeruginosa</named-content> Isolates

ABSTRACT Variation in the genome of Pseudomonas aeruginosa, an important pathogen, can have dramatic impacts on the bacterium’s ability to cause disease. We therefore asked whether it was possible to predict the virulence of P. aeruginosa isolates based on their genomic content. We applied a machine...

Full description

Saved in:
Bibliographic Details
Main Authors: Nathan B. Pincus, Egon A. Ozer, Jonathan P. Allen, Marcus Nguyen, James J. Davis, Deborah R. Winter, Chih-Hsien Chuang, Cheng-Hsun Chiu, Laura Zamorano, Antonio Oliver, Alan R. Hauser
Format: article
Language:EN
Published: American Society for Microbiology 2020
Subjects:
Pseudomonas aeruginosa
genome analysis
machine learning
modeling
prediction
virulence
Microbiology
QR1-502
Online Access:https://doaj.org/article/1908f46e3086477ca33102afad2a73da
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://doaj.org/article/1908f46e3086477ca33102afad2a73da

Similar Items