Λεπτομέρειες βιβλιογραφικής εγγραφής
| Τίτλος: |
Predicting antibiotic resistance genes and bacterial phenotypes based on protein language models. |
| 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] |
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| Βάση Δεδομένων: |
Complementary Index |