Integrating natural language processing and genome analysis enables accurate bacterial phenotype prediction.

ABSTRACT: Understanding microbial phenotypes from genomic data is crucial for studying co-evolution, ecology, and pathology. This study presents a scalable approach that integrates literature-extracted information with genomic data, combining natural language processing and functional genome analysis. We applied this method to publicly available data, providing novel insights into predicting microbial phenotypes. We fine-tuned transformer-based language models to analyze 3.83 million open-access scientific articles, extracting a phenotypic network of bacterial strains. This network maps relationships between strains and traits such as pathogenicity, metabolism, and biome preference. By annotating their reference genomes, we predicted key genes influencing these traits. Our findings align with known

SUBMITTER: Gomez-Perez D 

PROVIDER: S-EPMC12746109 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature