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However, current research relies on K-mer, which limits the capture of structurally and functionally relevant gene contexts. Moreover, these approaches struggle with encoding biologically meaningful genes and fail to address the one-to-many and many-to-one relationships inherent in metagenomic data. To overcome these challenges, we introduce FGeneBERT, a novel metagenomic pre-trained model that employs a protein-based gene representation as a context-aware and structure-relevant tokenizer. FGeneBERT incorporates masked gene modeling to enhance the understanding of inter-gene contextual relationships and triplet enhanced metagenomic contrastive learning to elucidate gene sequence\u2013function relationships. Pre-trained on over 100 million metagenomic sequences, FGeneBERT demonstrates superior performance on metagenomic datasets at four levels, spanning gene, functional, bacterial, and environmental levels and ranging from 1 to 213 k input sequences. Case studies of ATP synthase and gene operons highlight FGeneBERT\u2019s capability for functional recognition and its biological relevance in metagenomic research.<\/jats:p>","DOI":"10.1093\/bib\/bbaf592","type":"journal-article","created":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T12:19:16Z","timestamp":1762777156000},"source":"Crossref","is-referenced-by-count":2,"title":["FGeneBERT: function-driven pre-trained gene language model for metagenomics"],"prefix":"10.1093","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-5480-4272","authenticated-orcid":false,"given":"Chenrui","family":"Duan","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, Zhejiang University , No. 866, Yuhangtang Road, 310058 Zhejiang ,","place":["P. R. China"]},{"name":"School of Engineering, Westlake University , No. 600 Dunyu Road, 310030 Zhejiang ,","place":["P. R. 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