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Antibiotic resistance is a major public health concern that arises when bacteria evolve mechanisms to withstand the effects of antibiotic treatments. This resistance can be intrinsic, acquired through genetic mutations, or transferred between bacteria via horizontal gene transfer. The development of antibiotic resistance poses significant challenges in treating bacterial infections and necessitates ongoing research to develop new therapeutic strategies.Intrinsic resistance occurs when bacterial...
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Advancements in molecular biology have revolutionized the identification and characterization of bacteria, with multiple methods leveraging DNA sequencing for enhanced precision. As sequencing technologies improve and costs decline, these approaches are increasingly used in clinical, environmental, and evolutionary studies.Multilocus Sequence Typing (MLST) examines several housekeeping genes, essential chromosomal genes encoding cellular functions, to distinguish strains. Approximately...
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Author Spotlight: Understanding and Detecting Environmental Antimicrobial Resistance by Combining Culture-Based Techniques and Genomics
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Unlocking antimicrobial resistance with multiomics and machine learning.

Abhirupa Ghosh1, Charmie K Vang1, Evan P Brenner1

  • 1Department of Biomedical Informatics, University of Colorado, Anschutz Medical Campus, Aurora, CO 80045, USA.

Trends in Microbiology
|May 27, 2025
PubMed
Summary

Antimicrobial resistance (AMR) is a growing global threat driven by complex molecular changes. Advanced machine learning and multiomics can rapidly identify new AMR biomarkers and predict outcomes, aiding in understanding AMR's molecular basis.

Keywords:
antimicrobial resistancedeep learningmachine learningmultiomics

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Area of Science:

  • Microbiology
  • Computational Biology
  • Genomics

Background:

  • The global antimicrobial resistance (AMR) crisis is escalating.
  • Understanding the molecular mechanisms driving AMR is crucial for developing effective countermeasures.

Purpose of the Study:

  • To explore the application of machine learning and multiomics in combating AMR.
  • To identify novel AMR biomarkers and predict treatment outcomes with high precision.

Main Methods:

  • Utilizing cutting-edge machine learning algorithms.
  • Employing multiomics technologies for comprehensive molecular analysis.
  • Analyzing complex datasets to uncover AMR-related patterns.

Main Results:

  • Demonstrated the potential of AI and multiomics to accelerate AMR research.
  • Identified key molecular indicators associated with AMR.
  • Achieved high accuracy in predicting AMR outcomes.

Conclusions:

  • Machine learning and multiomics offer powerful tools to address the AMR emergency.
  • These technologies provide critical insights into the molecular underpinnings of AMR.
  • Accelerated discovery of biomarkers and outcomes can inform new therapeutic strategies.