Genus-level transfer learning of Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry data predicts antibiotic resistance with greater accuracy

Bacterial resistance, driven by excessive antibiotic use, has rendered many traditional antibiotics ineffective. Despite the advantages of applying matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) to predict bacterial antimicrobial resistance, limited databases and a lack of high-quality data hinder this effort. This study aimed to address this lack of data by integrating MALDI-TOF MS technology with transfer learning approaches, utilizing genus-level data, to predict species-specific bacterial antimicrobial resistance. The data were retrieved from the DRIAMS dataset. A multilayer perceptron deep neural network was applied for pretraining and fine-tuning, integrating genus-level data to enhance the accuracy of the species-specific predictions. Notably, fine-tuned models enhanced antibiotic resistance prediction. In Staphylococcus aureus , the area under the receiver operating characteristic curve (AUROC) improved from 0.95 to 0.96 and the area under the precision-recall curve (AUPRC) improved from 0.85 to 0.88 for oxacillin; for fusidic acid, the AUROC improved from 0.80 to 0.81 and the AUPRC from 0.32 to 0.36; and for ciprofloxacin, the AUROC improved from 0.80 to 0.82 while the AUPRC remained 0.57. In Klebsiella pneumoniae , the AUROC held steady at 0.75, while the AUPRC declined from 0.45 to 0.41 for ciprofloxacin, indicating potential limitations for certain pathogen-antibiotic combinations. Despite such variability, the overall improvements across multiple strains highlight the potential of transfer learning in antimicrobial resistance prediction and underscore the need to expand genus-level databases to improve species-level diagnostic accuracy, thereby enhancing clinical diagnostics.