Construction and validation of a phenotypic prediction model for bacterial gentamicin resistance using deep learning...

The emergence of bacterial resistance to antibiotics poses a significant threat to human health; thus, there is an urgent need for new strategies in understanding the mechanisms of resistance and further fast prediction of it. Deep learning models offer promising solutions through analyzing genetic sequences in the prediction of bacterial resistance patterns. This study develops and validates a transformer-based deep learning model, DNABERT-2-117M, to predict gentamicin resistance in Klebsiella pneumoniae directly from whole-genome sequences. Our central methodological advance investigates the impact of the DNA tokenization strategy on predictive performance. We prospectively compared a dynamic tokenization approach against conventional fixed-length tokenization. Evaluated through rigorous fivefold cross-validation and on a hold-out test set, the model employing dynamic tokenization achieved superior performance, with a mean F1-score of 0.95 and an area under the curve of 0.97. Our findings establish that optimizing sub-sequence tokenization is crucial for model accuracy, and this dynamic tokenization approach significantly enhances model accuracy for antibiotic resistance prediction from genomic data. This genome-based predictive model represents a scalable and rapid alternative to traditional antibiotic susceptibility testing, offering the potential to accelerate clinical decision-making and improve patient outcomes in managing K. pneumoniae infections.IMPORTANCEThis study addresses a critical gap in diagnostic technologies for hypervirulent, antibiotic-resistant Klebsiella pneumoniae. We introduce a transformer-based deep learning framework that utilizes dynamic tokenization strategies to predict drug resistance directly from genomic sequences. The core significance of our work is the development of a robust genomic prediction model that serves as a foundational component for future diagnostic paradigms. The significance of this work lies in its potential to fundamentally alter clinical timelines. By decoupling resistance prediction from the requirement for phenotypic growth, our approach is a critical step toward next-generation workflows (e.g., clinical metagenomics) that could deliver a complete diagnostic and susceptibility report directly from a patient sample within hours. This represents a scalable, rapid diagnostic platform that promises to accelerate the administration of targeted treatment for high-risk K. pneumoniae infections, with a clear trajectory toward same-day, specimen-to-result diagnostics in the near future.