ArticleFrontiers in bioinformatics2026
A comparative analysis of graph neural networks and classical machine learning for carbapenem-resistance prediction and genomic interpretation in
Article in Frontiers in bioinformatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Introduction: Carbapenem-resistant Methods: 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. Results: 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 Discussion: 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.
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