Table 12_A comparative analysis of graph neural networks and classical machine learning for carbapenem-resistance prediction and genomic interpretation in Acinetobacter baumannii.csv

Although many resistance determinants have been identified, how they organize into coordinated systems remains unclear.</p>Methods<p>Using a final binary modeling dataset of 2,448 genomes, we benchmarked machine-learning and graph-based models and developed a framework that pairs accurate prediction with systems-level genomic interpretation.</p>Results<p>Across five random seeds and 10-fold stratified cross-validation, a Random Forest classifier achieved the strongest internal performance using a bag-of-genes representation, with an accuracy of 0.9328 ± 0.0175 and ROC-AUC of 0.9650 ± 0.0138.

Random Forest also generalized best in an independent BV-BRC Asia validation cohort of 2,500 genomes, achieving an accuracy of 0.7728 and ROC-AUC of 0.8392. In parallel, a Graph Convolutional Network trained on gene-adjacency graphs enabled interpretation through post hoc explainability, prioritizing genes consistent with a candidate three-layer resistance architecture: (i) canonical rapid-response resistance genes, (ii) a cell-surface “Sugar Shield” centered on GDP/UDP-epimerase, and (iii) a metabolic “Command Center” involving regulators such as DnaA and quinone oxidoreductase 1.