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Updated: Nov 12, 2025

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
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Machine learning and applications in microbiology.

Stephen J Goodswen1, Joel L N Barratt2, Paul J Kennedy3

  • 1School of Life Sciences, University of Technology Sydney (UTS), 15 Broadway, Ultimo, NSW 2007, Australia.

FEMS Microbiology Reviews
|March 16, 2021
PubMed
Summary

Machine learning (ML) is crucial for analyzing complex microbial data, offering insights previously unattainable. This review guides microbiologists in applying ML for tasks like disease diagnosis and drug discovery.

Keywords:
K-means clusteringclassificationmachine learningmicrobiologysupervised learningunsupervised learning

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Area of Science:

  • Microbiology
  • Bioinformatics
  • Computational Biology

Background:

  • Understanding microorganisms at a molecular level generates vast data, often exceeding human analytical capabilities.
  • Machine learning (ML) is an artificial intelligence application essential for uncovering patterns in complex biological datasets.
  • The application of ML in biology is rapidly expanding but often perceived as inaccessible to non-specialists.

Purpose of the Study:

  • To provide key insights for researchers beginning to practice machine learning in microbiology.
  • To review current applications of machine learning across various microbiological domains.
  • To demystify machine learning for microbiologists and encourage its adoption.

Main Methods:

  • This review synthesizes information on machine learning principles relevant to biological data analysis.
  • It evaluates existing literature on machine learning applications in microbiology.
  • Examples are drawn from diverse real-life microbiological research areas.

Main Results:

  • Machine learning enables prediction of drug targets and vaccine candidates.
  • ML aids in diagnosing infectious diseases caused by microorganisms.
  • It is used for classifying antimicrobial drug resistance and predicting disease outbreaks.
  • ML facilitates the exploration of complex microbial interactions.

Conclusions:

  • Machine learning is an indispensable tool for modern microbiology, enabling advanced data analysis and problem-solving.
  • This review serves as a foundational guide for microbiologists seeking to leverage ML.
  • Adoption of ML can accelerate discoveries and innovation within the field of microbiology.