Frontiers in bioinformatics · 2026
A comparative analysis of graph neural networks and classical machine learning for carbapenem-resistance prediction and genomic interpretation in Acinetobacter baumannii.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Shaik Irfan Sharif, Sarmadi Morteza
Аннотация
INTRODUCTION: 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.
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 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.
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.
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 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.
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.