Microllm: a structured information extraction tool using large language models and named entity recognition in microbiology.

Abstract

Understanding the characteristics of individual microorganisms is crucial for deciphering microbial community structures and enabling effective system manipulation. Large-scale analysis in microbial bioinformatics is hindered by the scarcity of high-quality annotated datasets, the labor-intensive, and time-consuming process of manual review, and the limitations of current natural language processing (NLP) technologies in effectively capturing comprehensive information. To tackle these challenges, we developed a new tool MicroLLM, which integrates fine-tuned large language models (LLMs) and bidirectional encoder representations from Transformers (BERT) to enhance the extraction of microbial phenotypic information. MicroLLM converts unstructured text into structured JSON objects, making the data more accessible and organized. By leveraging the strengths of LLMs and named entity recognition (NER), it efficiently extracts complex, multirelational, and multi-entity information. This method establishes a strong foundation for constructing large-scale structured knowledge bases, streamlining the creation of scientific knowledge, and advancing our understanding of microbial ecosystems and their impacts on health.