mSphere
(Aug 2021)
Machine Learning of Bacterial Transcriptomes Reveals Responses Underlying Differential Antibiotic Susceptibility
Anand V. Sastry,
Nicholas Dillon,
Amitesh Anand,
Saugat Poudel,
Ying Hefner,
Sibei Xu,
Richard Szubin,
Adam M. Feist,
Victor Nizet,
Bernhard Palsson
Affiliations
Anand V. Sastry
Department of Bioengineering, University of California—San Diego, La Jolla, California, USA
Nicholas Dillon
ORCiD Department of Pediatrics, University of California—San Diego, La Jolla, California, USA
Amitesh Anand
Department of Bioengineering, University of California—San Diego, La Jolla, California, USA
Saugat Poudel
Department of Bioengineering, University of California—San Diego, La Jolla, California, USA
Ying Hefner
Department of Bioengineering, University of California—San Diego, La Jolla, California, USA
Sibei Xu
Department of Bioengineering, University of California—San Diego, La Jolla, California, USA
Richard Szubin
Department of Bioengineering, University of California—San Diego, La Jolla, California, USA
Adam M. Feist
Department of Bioengineering, University of California—San Diego, La Jolla, California, USA
Victor Nizet
Department of Pediatrics, University of California—San Diego, La Jolla, California, USA
Bernhard Palsson
ORCiD Department of Bioengineering, University of California—San Diego, La Jolla, California, USA
DOI
https://doi.org/10.1128/mSphere.00443-21
Journal volume & issue
Vol. 6,
no. 4
Abstract
Read online
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
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