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Rapid Identification of Pathogens

MALDI-TOF MS has transformed clinical microbiology by offering a rapid and reliable method for pathogen identification. The traditional approach to microbial identification typically involves time-consuming culture techniques and biochemical tests, which can delay the initiation of appropriate antimicrobial therapy. MALDI-TOF MS avoids these delays by using characteristic ribosomal protein mass patterns of microbial cells, enabling accurate species-level identification within minutes.Principle...
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Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
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Next-Generation Target Discovery in ESKAPE Pathogens: An AI-Driven Framework from Omics-Based to Systems-Level

Eleonora Chines1,2, Adriana Antonina Tempesta1, Ludovica Boscarelli1

  • 1Department of Biomedical and Biotechnological Sciences, University of Catania, 95123 Catania, Italy.

Antibiotics (Basel, Switzerland)
|May 27, 2026
PubMed
Summary

Artificial intelligence (AI) aids in discovering antimicrobial resistance (AMR) and virulence factors in ESKAPE pathogens. Future AI applications will integrate multi-omics data for novel diagnostics and therapeutics against these critical infections.

Keywords:
ESKAPE pathogensantimicrobial resistanceartificial intelligence (AI)deep learningmachine learningnext-generation ESKAPE target discoverytarget prioritization

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Establishment and Optimization of a High Throughput Setup to Study Staphylococcus epidermidis and Mycobacterium marinum Infection as a Model for Drug Discovery
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Published on: June 26, 2014

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Establishment and Optimization of a High Throughput Setup to Study Staphylococcus epidermidis and Mycobacterium marinum Infection as a Model for Drug Discovery
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Establishment and Optimization of a High Throughput Setup to Study Staphylococcus epidermidis and Mycobacterium marinum Infection as a Model for Drug Discovery

Published on: June 26, 2014

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • ESKAPE pathogens (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter spp.) are a major cause of healthcare-associated infections due to antimicrobial resistance (AMR).
  • Genomic plasticity and adaptive responses accelerate the emergence and spread of resistance and virulence factors in these pathogens.
  • Artificial intelligence (AI) offers powerful tools for analyzing large biological datasets to identify molecular signatures linked to AMR and pathogenicity.

Purpose of the Study:

  • This review explores AI-driven frameworks for predicting antimicrobial targets in ESKAPE pathogens.
  • It focuses on AI approaches utilizing genomic and transcriptomic data, with potential for integrating multi-omics data.
  • The review discusses AI's evolution towards biologically interpretable inference for prioritizing resistance mechanisms, virulence factors, and antimicrobial targets.

Main Methods:

  • Leveraging genomic and transcriptomic data analysis.
  • Employing network-based and systems-level modeling.
  • Integrating multi-omics data layers for comprehensive analysis.

Main Results:

  • Current AI applications effectively prioritize resistance and virulence determinants using genomic, transcriptomic, and network data.
  • AI supports the discovery of novel antimicrobial agents, including small molecules and antimicrobial peptides.
  • AI aids in identifying potential antimicrobial targets within ESKAPE pathogens.

Conclusions:

  • AI shows promise in prioritizing resistance mechanisms and virulence factors in ESKAPE pathogens.
  • Further integration of multi-layer data and experimental validation is crucial for clinical translation.
  • Future AI advancements aim to develop next-generation diagnostics, therapeutics, and stewardship strategies against ESKAPE pathogens.