Harnessing Large Language Models to Advance Microbiome Research: From Sequence Analysis to Clinical Applications

The rapid development of large language models (LLMs) has revolutionized microbiome research by providing powerful tools for analyzing complex microbial data. Traditional methods often struggle with the volume and complexity of microbiome datasets, necessitating advanced computational techniques. LLMs, with their ability to model sequences and understand context, have enhanced microbial classification, functional prediction, and sequence analysis. These models also facilitate microbiome association mining, extracting meaningful microbe–disease and microbe–diet relationships from vast biomedical literature. In clinical diagnostics, LLMs support state recognition and disease detection by automating the extraction and integration of microbiome data. Despite their promise, challenges remain, including improving model interpretability, cross‐dataset generalization, and integrating multiomics data. This review highlights the transformative potential of LLMs in microbiome research, emphasizing their applications in sequence profiling, association mining, and clinical diagnostics, and discusses future directions for enhancing their effectiveness in precision medicine and ecological studies.