ArticleBioinformatics (Oxford, England)2023
PLM-ARG: antibiotic resistance gene identification using a pretrained protein language model.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Computational Genomics for Resistome Characterization: Current Advancements and Future Challenges Under a One Health Perspective.Antibiotics (Basel, Switzerland) · 2026Review
- Conserved protein sequence-structure signatures identify antibiotic resistance genes from the human microbiome.Microbiome · 2026Article
- Profiling the gut resistome to unlock antimicrobial resistance biology and inform clinical risk.Nature communications · 2026Review
- A review of computational approaches for metagenomics by long-read sequencing.Science China. Life sciences · 2026Review
- Artificial intelligence for antimicrobial resistance: advancing reproducibility, interpretability, and clinical deployment.Briefings in bioinformatics · 2026Review
- Protein language model embeddings improve HIV drug resistance prediction: a comprehensive benchmark with attention-based interpretability.Bioinformatics (Oxford, England) · 2026Article
- Artificial intelligence in microbiology: implications for metagenomics, diagnostics, and AMR surveillance.Biomedical engineering online · 2026Review
- Artificial intelligence for early detection and risk prediction of antimicrobial resistance in aquatic ecosystems.npj antimicrobials and resistance · 2026Review
- Computational mapping of resistance-relevant signals in proteins using deep and classical feature spaces.Frontiers in microbiology · 2026Article
- Advances in deep learning for antimicrobial research.Frontiers in microbiology · 2026Review
- Artificial intelligence in protein-based detection and inhibition of AMR pathways.Journal of computer-aided molecular design · 2025Review
- Machine Learning-Based Characterization ofPathogens (Basel, Switzerland) · 2025Article
- DeepSEA: an alignment-free explainable approach to annotate antimicrobial resistance proteins.BMC bioinformatics · 2025Article
- ProtAlign-ARG: antibiotic resistance gene characterization integrating protein language models and alignment-based scoring.Scientific reports · 2025Article
- The risk of pathogenicity and antibiotic resistance in deep-sea cold seep microorganisms.mSystems · 2025Article
- VirulentHunter: deep learning-based virulence factor predictor illuminates pathogenicity in diverse microbial contexts.Briefings in bioinformatics · 2025Article
- Deciphering the biosynthetic potential of microbial genomes using a BGC language processing neural network model.Nucleic acids research · 2025Article
- DRAMMA: a multifaceted machine learning approach for novel antimicrobial resistance gene detection in metagenomic data.Microbiome · 2025Article
- Predicting the bacterial host range of plasmid genomes using the language model-based one-class support vector machine algorithm.Microbial genomics · 2025Article
- Biochemistry and Future Perspectives of Antibiotic Resistance: An Eye on Active Natural Products.Antibiotics (Basel, Switzerland) · 2024Review
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Authors and funding
10 authors.
Funding
Abstract
motivationAntibiotic resistance presents a formidable global challenge to public health and the environment. While considerable endeavors have been dedicated to identify antibiotic resistance genes (ARGs) for assessing the threat of antibiotic resistance, recent extensive investigations using metagenomic and metatranscriptomic approaches have unveiled a noteworthy concern. A significant fraction of proteins defies annotation through conventional sequence similarity-based methods, an issue that extends to ARGs, potentially leading to their under-recognition due to dissimilarities at the sequence level.
resultsHerein, we proposed an Artificial Intelligence-powered ARG identification framework using a pretrained large protein language model, enabling ARG identification and resistance category classification simultaneously. The proposed PLM-ARG was developed based on the most comprehensive ARG and related resistance category information (>28K ARGs and associated 29 resistance categories), yielding Matthew's correlation coefficients (MCCs) of 0.983 ± 0.001 by using a 5-fold cross-validation strategy. Furthermore, the PLM-ARG model was verified using an independent validation set and achieved an MCC of 0.838, outperforming other publicly available ARG prediction tools with an improvement range of 51.8%-107.9%. Moreover, the utility of the proposed PLM-ARG model was demonstrated by annotating resistance in the UniProt database and evaluating the impact of ARGs on the Earth's environmental microbiota. AVAILABILITY AND IMPLEMENTATION: PLM-ARG is available for academic purposes at https://github.com/Junwu302/PLM-ARG, and a user-friendly webserver (http://www.unimd.org/PLM-ARG) is also provided.
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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.