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Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
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Published on: December 7, 2021

Responsible Use of Large Language Models in Microbial Genomics and Bioinformatics: A Life-Science Framework for

Mia Yang Ang1,2,3, Li Chen3,4, Lanni Song3,4

  • 1Department of Biomedical Sciences, Jeffrey Cheah Sunway Medical School, Faculty of Medical and Life Sciences, Sunway University, Sunway City, Petaling Jaya 47500, Selangor, Malaysia.

Life (Basel, Switzerland)
|June 26, 2026
PubMed
Summary
This summary is machine-generated.

Large language models (LLMs) offer powerful tools for microbial genomics research, but carry risks like inaccurate biological claims. A new framework, MicrobeGuardGPT, promotes responsible LLM use through verification and expert validation.

Keywords:
antimicrobial resistancebenchmarkingbioinformaticscomputational biologylarge language modelsmetagenomicsmicrobial genomicsmicrobiomereproducibilityresponsible AI

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Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

Area of Science:

  • Bioinformatics and Microbial Genomics
  • Artificial Intelligence in Life Sciences

Background:

  • Large language models (LLMs) are increasingly utilized in life sciences for tasks including writing, coding, and data interpretation.
  • Microbial genomics research involves complex data integration (e.g., genome annotations, resistance profiles) making LLMs appealing but risky due to potential inaccuracies.

Purpose of the Study:

  • To review the responsible application of LLMs in microbial genomics.
  • To introduce a conceptual framework (MicrobeGuardGPT) for assessing the reliability of LLM-generated outputs in this domain.

Main Methods:

  • A narrative review of LLM applications in microbial genomics, covering areas like genome annotation, antimicrobial resistance, and microbiome analysis.
  • Development of the MicrobeGuardGPT framework, focusing on task domains, evidence verification, expert validation, and reliability classification.

Main Results:

  • LLMs present significant risks in microbial genomics, including fabricated data, citation errors, and unreliable inferences.
  • The MicrobeGuardGPT framework provides a structured approach to mitigate these risks by emphasizing verification and expert oversight.

Conclusions:

  • Responsible LLM integration in microbial genomics requires domain-specific benchmarks, curated data linkage, transparent reporting, and robust human oversight.
  • Adoption of frameworks like MicrobeGuardGPT is crucial for safe and effective use of LLMs in research, diagnostics, and public health applications.