Machine learning for microbiologists.

Journal: Nature reviews. Microbiology
Published Date:

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.

Authors

  • Francesco Asnicar
    Department of Cellular, Computational and Integrative Biology, University of Trento, Trento, Italy.
  • Andrew Maltez Thomas
    Department of Cellular, Computational and Integrative Biology, University of Trento, Trento, Italy.
  • Andrea Passerini
    Department of Information Engineering and Computer Science, University of Trento, Trento, Italy.
  • Levi Waldron
    Graduate School of Public Health and Health Policy, City University of New York, New York, New York, United States of America.
  • Nicola Segata
    Centre for Integrative Biology, University of Trento, Trento, Italy.