Machine learning for microbiologists

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 i...

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Published inNature reviews. Microbiology Vol. 22; no. 4; pp. 191 - 205
Main Authors Asnicar, Francesco, Thomas, Andrew Maltez, Passerini, Andrea, Waldron, Levi, Segata, Nicola
Format Journal Article
LanguageEnglish
Published London Nature Publishing Group UK 01.04.2024
Nature Publishing Group
Subjects
631/114/1305
631/114/2397
631/326
Algorithms
Antibiotic resistance
Antibiotics
Antimicrobial agents
Biomedical and Life Sciences
Classification
Clustering
Computer science
Drug resistance
Feature selection
Genes
Genomes
Humans
Infectious Diseases
Learning algorithms
Life Sciences
Machine Learning
Medical Microbiology
Microbiology
Microbiomes
Microbiota
Oncology
Parasitology
Review Article
Software
Taxonomy
Tuberculosis
Virology
Online AccessGet full text
ISSN1740-1526
1740-1534
1740-1534
DOI10.1038/s41579-023-00984-1

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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. In this Review, Segata, Waldron and colleagues discuss important key concepts of machine learning that are relevant to microbiologists and provide them with a set of tools essential to apply machine learning in microbiology research.
AbstractList 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.In this Review, Segata, Waldron and colleagues discuss important key concepts of machine learning that are relevant to microbiologists and provide them with a set of tools essential to apply machine learning in microbiology research.
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 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 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 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. In this Review, Segata, Waldron and colleagues discuss important key concepts of machine learning that are relevant to microbiologists and provide them with a set of tools essential to apply machine learning in microbiology research.
Author Asnicar, Francesco
Segata, Nicola
Thomas, Andrew Maltez
Passerini, Andrea
Waldron, Levi
AuthorAffiliation 2 Department of Information Engineering and Computer Science, University of Trento, Trento, Italy
5 These authors contributed equally: Francesco Asnicar, Andrew Maltez Thomas
4 Department of Experimental Oncology, European Institute of Oncology IRCCS, Milan, Italy
1 Department of Cellular, Computational and Integrative Biology, University of Trento, Trento, Italy
3 Department of Epidemiology and Biostatistics, City University of New York, New York, NY, USA
AuthorAffiliation_xml – name: 2 Department of Information Engineering and Computer Science, University of Trento, Trento, Italy
– name: 4 Department of Experimental Oncology, European Institute of Oncology IRCCS, Milan, Italy
– name: 3 Department of Epidemiology and Biostatistics, City University of New York, New York, NY, USA
– name: 1 Department of Cellular, Computational and Integrative Biology, University of Trento, Trento, Italy
– name: 5 These authors contributed equally: Francesco Asnicar, Andrew Maltez Thomas
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  surname: Segata
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  email: nicola.segata@unitn.it
  organization: Department of Cellular, Computational and Integrative Biology, University of Trento, Department of Experimental Oncology, European Institute of Oncology IRCCS
BackLink https://www.ncbi.nlm.nih.gov/pubmed/37968359$$D View this record in MEDLINE/PubMed
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N.S., F.A. and A.M.T. contributed equally to all aspects of the article. A.P. contributed substantially to discussion of the content and reviewed and/or edited the manuscript before submission. L.W. contributed substantially to discussion of the content, writing, and review and/or editing of the manuscript before submission.
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PublicationTitle Nature reviews. Microbiology
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631/114/2397
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Algorithms
Antibiotic resistance
Antibiotics
Antimicrobial agents
Biomedical and Life Sciences
Classification
Clustering
Computer science
Drug resistance
Feature selection
Genes
Genomes
Humans
Infectious Diseases
Learning algorithms
Life Sciences
Machine Learning
Medical Microbiology
Microbiology
Microbiomes
Microbiota
Oncology
Parasitology
Review Article
Software
Taxonomy
Tuberculosis
Virology
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