Machine Learning of Bacterial Transcriptomes Reveals Responses Underlying Differential Antibiotic Susceptibility-Reference-Cited by-同舟云学术

Machine Learning of Bacterial Transcriptomes Reveals Responses Underlying Differential Antibiotic Susceptibility

Published:2021-08-25 Issue:4 Volume:6 Page:
ISSN:2379-5042
Container-title:mSphere
language:en
Short-container-title:mSphere

Author:

Sastry Anand V.¹,Dillon Nicholas²³⁴^ORCID,Anand Amitesh¹,Poudel Saugat¹,Hefner Ying¹,Xu Sibei¹,Szubin Richard¹,Feist Adam M.¹⁵,Nizet Victor²³,Palsson Bernhard¹²⁵^ORCID

Affiliation:

1. Department of Bioengineering, University of California—San Diego, La Jolla, California, USA

2. Department of Pediatrics, University of California—San Diego, La Jolla, California, USA

3. Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California—San Diego, La Jolla, California, USA

4. Department of Biological Sciences, University of Texas at Dallas, Richardson, Texas, USA

5. Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark

Abstract

Antibiotic resistance is an imminent threat to global health. Patient treatment regimens are often selected based on results from standardized antibiotic susceptibility testing (AST) in the clinical microbiology lab, but these in vitro tests frequently misclassify drug effectiveness due to their poor resemblance to actual host conditions.

Funder

HHS | NIH | National Institute of Allergy and Infectious Diseases

HHS | National Institutes of Health

Novo Nordisk Fonden

Publisher

American Society for Microbiology

Subject

Molecular Biology,Microbiology

Link

https://journals.asm.org/doi/pdf/10.1128/mSphere.00443-21

Reference53 articles.

1. Targeting Antibiotic Tolerance, Pathogen by Pathogen