Artificial intelligence for microbiology and microbiome research.

Advancements in artificial intelligence (AI) have transformed many scientific fields, with microbiology and microbiome research now experiencing significant breakthroughs through machine-learning applications. This review provides a comprehensive overview of AI-driven approaches tailored for microbiology and microbiome studies, emphasizing both technical advancements and biological insights. We first introduce foundational AI techniques and offer guidance on choosing between traditional machine-learning and sophisticated deep-learning methods based on specific research goals. The primary section on application scenarios spans diverse research areas from taxonomic profiling, functional annotation and prediction, microbe-X interactions, microbial ecology, metabolic modeling, precision nutrition, and clinical microbiology to prevention and therapeutics. Finally, we discuss challenges in this field and highlight some recent breakthroughs. Together, this review underscores AI's transformative role in microbiology and microbiome research, paving the way for innovative methodologies and applications that enhance our understanding of microbial life and its impact on our planet and our health.

• Publication year

  2026

• Venue

  Cell Systems

• Publication date

  2026-02-01

• Fields of study

  Biology, Medicine, Computer Science, Environmental Science

• Identifiers
  DOI 10.1016/j.cels.2026.101531PMID 41713400PMCID PMC12927609
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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