Artificial Intelligence for Microbiology and Microbiome Research

TL;DR

This paper surveys how artificial intelligence, especially machine learning and deep learning, is transforming microbiology and microbiome research by covering foundational techniques, data modalities, and a broad set of application areas. It highlights concrete methods across metagenomics, functional annotation, microbe interactions, ecology, metabolism, precision nutrition, clinical microbiology, and therapeutics, with named models and frameworks. The authors discuss critical challenges—interpretability, small-sample regimes, and benchmarking—and point to recent breakthroughs (e.g., AlphaFold-3, Evo) as catalysts for causal, multiscale understanding. Overall, the article emphasizes AI’s potential to turn descriptive microbiome analyses into predictive, mechanistic, and clinically actionable insights.

Abstract

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 begin with an introduction to foundational AI techniques, including primary machine learning paradigms and various deep learning architectures, 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 \& prediction, microbe-X interactions, microbial ecology, metabolic modeling, precision nutrition, clinical microbiology, to prevention \& 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.

Figures (1)

• Figure 1: Application scenarios of AI in microbiology and microbiome research. The application scenarios of AI in microbiology and microbiome research are diverse, spanning (1) taxonomic profiling (e.g., metagenome assembly, binning, taxonomic classification, and nanopore sequencing basecalling), (2) functional annotation and prediction (including gene prediction, antibiotic resistance gene and plasmid identification, biosynthetic gene clusters prediction, 16S rRNA copy number prediction and mutation/evolution prediction), (3) microbe-host/disease/drug interactions, (4) microbial ecology (such as microbial interaction networks, composition and colonization outcome predictions and keystone species identification), (5) metabolic modeling, (6) precision nutrition (e.g., nutrition profile correction, metabolomic profile prediction and personalized diet recommendation), (7) clinical microbiology (such as microorganism detection, identification and quantification, antimicrobial susceptibility evaluation and disease diagnosis, classification, and clinical outcome prediction), and (8) applications in prevention and therapeutics (including peptides identification and generation, probiotic mining, antibiotic discovery, phage therapy and vaccine design).