Academic Journal
Integrating natural language processing and genome analysis enables accurate bacterial phenotype prediction.
| Τίτλος: | Integrating natural language processing and genome analysis enables accurate bacterial phenotype prediction. |
|---|---|
| Συγγραφείς: | Gómez-Pérez D; Cellular and Organismic Networks , Faculty of Biology, Ludwig Maximilian University of Munich, 82152 Munich, Germany., Keller A; Cellular and Organismic Networks , Faculty of Biology, Ludwig Maximilian University of Munich, 82152 Munich, Germany. |
| Πηγή: | NAR genomics and bioinformatics [NAR Genom Bioinform] 2025 Dec 29; Vol. 7 (4), pp. lqaf174. Date of Electronic Publication: 2025 Dec 29 (Print Publication: 2025). |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Oxford University Press Country of Publication: England NLM ID: 101756213 Publication Model: eCollection Cited Medium: Internet ISSN: 2631-9268 (Electronic) Linking ISSN: 26319268 NLM ISO Abbreviation: NAR Genom Bioinform Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: [Oxford] : Oxford University Press, [2019]- |
| Ιατρικοί όροι (MeSH): | Bacteria*/genetics , Genomics*/methods , Natural Language Processing* , Genome, Bacterial*, Phenotype ; Machine Learning |
| Περίληψη: | 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 phenotypes, reveal novel correlations, and uncover genes involved in disease and host associations. The network's interconnectivity provides deeper understanding of microbial communities and allowed identification of hub species through inferred trophic connections that are difficult to infer experimentally. This work demonstrates the potential of machine learning for uncovering cross-species gene-phenotype patterns. As microbial genomic data and literature expand, such methods will be essential for extracting meaningful insights and advancing microbiology research. In summary, this integrative approach can accelerate discovery and understanding in microbial genomics. Ultimately, such techniques will facilitate the study of microbial ecology, co-evolutionary processes, and disease pathogenesis to an unprecedented depth. (© The Author(s) 2025. Published by Oxford University Press.) |
| Competing Interests: | No competing interest is declared. |
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| Entry Date(s): | Date Created: 20251231 Date Completed: 20251231 Latest Revision: 20260102 |
| Update Code: | 20260130 |
| PubMed Central ID: | PMC12746109 |
| DOI: | 10.1093/nargab/lqaf174 |
| PMID: | 41472882 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 2631-9268 |
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| DOI: | 10.1093/nargab/lqaf174 |