This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility:


peer-reviewed publication

trusted source


Review examines machine learning concepts for microbiologists

Review examines machine learning concepts for microbiologists
General workflow and examples for machine learning applications in microbiology. Credit: Nature Reviews Microbiology (2023). DOI: 10.1038/s41579-023-00984-1

In a review in Nature Reviews Microbiology, Professor Levi Waldron and colleagues highlight the increasing importance of machine learning in microbiology, where it is used for tasks such as predicting antibiotic resistance and associating human microbiome features with complex host diseases.

Together with co-authors from the University of Trento and the European Institute for Oncology in Italy, Waldron examines the main machine learning concepts, tasks, and applications that are relevant for experimental and clinical microbiologists. The review provides the minimal toolbox for a to be able to critically evaluate and apply machine learning in their field.

"It was exciting to try to distill the essential concepts of machine learning for a broad audience of microbiologists, and to do it as part of a team with so much expertise," says Waldron. "I think this review will also be interesting for other public health professionals outside the field of , who just would like a conceptual, comprehensible, but rigorous overview of machine learning."

More information: Francesco Asnicar et al, Machine learning for microbiologists, Nature Reviews Microbiology (2023). DOI: 10.1038/s41579-023-00984-1

Journal information: Nature Reviews Microbiology

Citation: Review examines machine learning concepts for microbiologists (2023, December 6) retrieved 4 March 2024 from
This document is subject to copyright. Apart from any fair dealing for the purpose of private study or research, no part may be reproduced without the written permission. The content is provided for information purposes only.

Explore further

Research team envisions a bright future with active machine learning in chemical engineering


Feedback to editors