A comparative analysis of graph neural networks and classical machine learning for carbapenem-resistance prediction...

Carbapenem-resistant Acinetobacter baumannii is a hospital-associated pathogen linked to high mortality and limited treatment options. Although many resistance determinants have been identified, how they organize into coordinated systems remains unclear.

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.

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. Explanation-stability analysis identified a reproducible consensus subset while supporting cautious interpretation of individual rankings. Complementary computational analyses, including protein-protein interaction enrichment, Gene Ontology enrichment, structural modeling, and molecular docking, supported the biological coherence and structural plausibility of the proposed architecture.

Together, these findings suggest that carbapenem resistance may reflect a coordinated, multi-layer defense system and nominate GDP/UDP-epimerase and quinone oxidoreductase 1 as candidates for future experimental investigation.