Academic Journal
Predicting antibiotic resistance genes and bacterial phenotypes based on protein language models.
| Τίτλος: | Predicting antibiotic resistance genes and bacterial phenotypes based on protein language models. |
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| Συγγραφείς: | Wang, Boqian, Meng, Renjie, Li, Zhong, Hu, Mingda, Wang, Xin, Zhao, Yunxiang, Chai, Zili, Jin, Yuan, Yue, Junjie, Chen, Wei, Ren, Hongguang |
| Πηγή: | Frontiers in Microbiology; 2025, p1-14, 14p |
| Θεματικοί όροι: | Drug resistance in bacteria, Deep learning, Protein models, Diagnostic services, Data augmentation, Recurrent neural networks |
| Περίληψη: | Introduction: Antibiotic resistance is emerging as a critical global public health threat. The precise prediction of bacterial antibiotic resistance genes (ARGs) and phenotypes is essential to understand resistance mechanisms and guide clinical antibiotic use. Although high-throughput DNA sequencing provides a foundation for identification, current methods lack precision and often require manual intervention. Methods: We developed a novel deep learning model for ARG prediction by integrating bacterial protein sequences using two protein language models, ProtBert-BFD and ESM-1b. The model further employs data augmentation techniques and Long Short-Term Memory (LSTM) networks to enhance feature extraction and classification performance. Results: The proposed model demonstrated superior performance compared to existing methods, achieving higher accuracy, precision, recall, and F1-score. It significantly reduced both false negative and false positive predictions in identifying ARGs, providing a robust computational tool for reliable gene-level resistance detection. Moreover, the model was successfully applied to predict bacterial resistance phenotypes, demonstrating its potential for clinical applicability. Discussion: This study presents an accurate and automated approach for predicting antibiotic resistance genes and phenotypes, reducing the need for manual verification. The model offers a powerful technical tool that can support clinical decision-making and guide antibiotic use, thereby addressing an urgent need in the fight against antimicrobial resistance. [ABSTRACT FROM AUTHOR] |
| Copyright of Frontiers in Microbiology is the property of Frontiers Media S.A. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
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| Items | – Name: Title Label: Title Group: Ti Data: Predicting antibiotic resistance genes and bacterial phenotypes based on protein language models. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Wang%2C+Boqian%22">Wang, Boqian</searchLink><br /><searchLink fieldCode="AR" term="%22Meng%2C+Renjie%22">Meng, Renjie</searchLink><br /><searchLink fieldCode="AR" term="%22Li%2C+Zhong%22">Li, Zhong</searchLink><br /><searchLink fieldCode="AR" term="%22Hu%2C+Mingda%22">Hu, Mingda</searchLink><br /><searchLink fieldCode="AR" term="%22Wang%2C+Xin%22">Wang, Xin</searchLink><br /><searchLink fieldCode="AR" term="%22Zhao%2C+Yunxiang%22">Zhao, Yunxiang</searchLink><br /><searchLink fieldCode="AR" term="%22Chai%2C+Zili%22">Chai, Zili</searchLink><br /><searchLink fieldCode="AR" term="%22Jin%2C+Yuan%22">Jin, Yuan</searchLink><br /><searchLink fieldCode="AR" term="%22Yue%2C+Junjie%22">Yue, Junjie</searchLink><br /><searchLink fieldCode="AR" term="%22Chen%2C+Wei%22">Chen, Wei</searchLink><br /><searchLink fieldCode="AR" term="%22Ren%2C+Hongguang%22">Ren, Hongguang</searchLink> – Name: TitleSource Label: Source Group: Src Data: Frontiers in Microbiology; 2025, p1-14, 14p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Drug+resistance+in+bacteria%22">Drug resistance in bacteria</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Protein+models%22">Protein models</searchLink><br /><searchLink fieldCode="DE" term="%22Diagnostic+services%22">Diagnostic services</searchLink><br /><searchLink fieldCode="DE" term="%22Data+augmentation%22">Data augmentation</searchLink><br /><searchLink fieldCode="DE" term="%22Recurrent+neural+networks%22">Recurrent neural networks</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Introduction: Antibiotic resistance is emerging as a critical global public health threat. The precise prediction of bacterial antibiotic resistance genes (ARGs) and phenotypes is essential to understand resistance mechanisms and guide clinical antibiotic use. Although high-throughput DNA sequencing provides a foundation for identification, current methods lack precision and often require manual intervention. Methods: We developed a novel deep learning model for ARG prediction by integrating bacterial protein sequences using two protein language models, ProtBert-BFD and ESM-1b. The model further employs data augmentation techniques and Long Short-Term Memory (LSTM) networks to enhance feature extraction and classification performance. Results: The proposed model demonstrated superior performance compared to existing methods, achieving higher accuracy, precision, recall, and F1-score. It significantly reduced both false negative and false positive predictions in identifying ARGs, providing a robust computational tool for reliable gene-level resistance detection. Moreover, the model was successfully applied to predict bacterial resistance phenotypes, demonstrating its potential for clinical applicability. Discussion: This study presents an accurate and automated approach for predicting antibiotic resistance genes and phenotypes, reducing the need for manual verification. The model offers a powerful technical tool that can support clinical decision-making and guide antibiotic use, thereby addressing an urgent need in the fight against antimicrobial resistance. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Frontiers in Microbiology is the property of Frontiers Media S.A. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3389/fmicb.2025.1628952 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 1 Subjects: – SubjectFull: Drug resistance in bacteria Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Protein models Type: general – SubjectFull: Diagnostic services Type: general – SubjectFull: Data augmentation Type: general – SubjectFull: Recurrent neural networks Type: general Titles: – TitleFull: Predicting antibiotic resistance genes and bacterial phenotypes based on protein language models. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Wang, Boqian – PersonEntity: Name: NameFull: Meng, Renjie – PersonEntity: Name: NameFull: Li, Zhong – PersonEntity: Name: NameFull: Hu, Mingda – PersonEntity: Name: NameFull: Wang, Xin – PersonEntity: Name: NameFull: Zhao, Yunxiang – PersonEntity: Name: NameFull: Chai, Zili – PersonEntity: Name: NameFull: Jin, Yuan – PersonEntity: Name: NameFull: Yue, Junjie – PersonEntity: Name: NameFull: Chen, Wei – PersonEntity: Name: NameFull: Ren, Hongguang IsPartOfRelationships: – BibEntity: Dates: – D: 22 M: 09 Text: 2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 1664302X Titles: – TitleFull: Frontiers in Microbiology Type: main |
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