ArticleNature communications2022
Deciphering microbial gene function using natural language processing.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed, 56 citations in OpenAlex.
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- Word embeddings as autonomous predictors in materials design-the effect of inherent variability on information transfer.Journal of cheminformatics · 2026Article
- Artificial intelligence for improving decision-making in bacterial infection management: a narrative review.The Journal of antimicrobial chemotherapy · 2026Review
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- Cutting-edge deep-learning based tools for metagenomic research.National science review · 2025Review
- Throw out an oligopeptide to catch a protein: Deep learning and natural language processing-screened tripeptide PSP promotes Osteolectin-mediated vascularized bone regeneration.Bioactive materials · 2025Article
- Enhancing nucleotide sequence representations in genomic analysis with contrastive optimization.Communications biology · 2025Article
- MINE: a new way to design genetics experiments for discovery.Briefings in bioinformatics · 2025Article
- FGeneBERT: function-driven pre-trained gene language model for metagenomics.Briefings in bioinformatics · 2025Article
- Molecular basis of foreign DNA recognition by BREX anti-phage immunity system.Nature communications · 2025Article
- Predicting the bacterial host range of plasmid genomes using the language model-based one-class support vector machine algorithm.Microbial genomics · 2025Article
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- Quest for Orthologs in the Era of Biodiversity Genomics.Genome biology and evolution · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Revealing the function of uncharacterized genes is a fundamental challenge in an era of ever-increasing volumes of sequencing data. Here, we present a concept for tackling this challenge using deep learning methodologies adopted from natural language processing (NLP). We repurpose NLP algorithms to model "gene semantics" based on a biological corpus of more than 360 million microbial genes within their genomic context. We use the language models to predict functional categories for 56,617 genes and find that out of 1369 genes associated with recently discovered defense systems, 98% are inferred correctly. We then systematically evaluate the "discovery potential" of different functional categories, pinpointing those with the most genes yet to be characterized. Finally, we demonstrate our method's ability to discover systems associated with microbial interaction and defense. Our results highlight that combining microbial genomics and language models is a promising avenue for revealing gene functions in microbes.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.