Machine learning is increasingly important in microbiology where it is used for tasks such as predicting antibiotic resistance and associating human microbiome features with complex host diseases. The applications in microbiology are quickly expanding and the machine learning tools frequently used in basic and clinical research range from classification and regression to clustering and dimensionality reduction. In this Review, we examine the main machine learning concepts, tasks and applications that are relevant for experimental and clinical microbiologists. We provide the minimal toolbox for a microbiologist to be able to understand, interpret and use machine learning in their experimental and translational activities.

Machine learning for microbiologists / Asnicar, F.; Thomas, A. M.; Passerini, Andrea; Waldron, L.; Segata, N.. - In: NATURE REVIEWS MICROBIOLOGY. - ISSN 1740-1526. - 2024, 22:(2024), pp. 191-205. [10.1038/s41579-023-00984-1]

Machine learning for microbiologists

Asnicar, F.;Thomas, A. M.;Passerini, Andrea;Waldron, L.
;
Segata N.
2024-01-01

Abstract

Machine learning is increasingly important in microbiology where it is used for tasks such as predicting antibiotic resistance and associating human microbiome features with complex host diseases. The applications in microbiology are quickly expanding and the machine learning tools frequently used in basic and clinical research range from classification and regression to clustering and dimensionality reduction. In this Review, we examine the main machine learning concepts, tasks and applications that are relevant for experimental and clinical microbiologists. We provide the minimal toolbox for a microbiologist to be able to understand, interpret and use machine learning in their experimental and translational activities.
2024
Asnicar, F.; Thomas, A. M.; Passerini, Andrea; Waldron, L.; Segata, N.
Machine learning for microbiologists / Asnicar, F.; Thomas, A. M.; Passerini, Andrea; Waldron, L.; Segata, N.. - In: NATURE REVIEWS MICROBIOLOGY. - ISSN 1740-1526. - 2024, 22:(2024), pp. 191-205. [10.1038/s41579-023-00984-1]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/400731
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