GraphGDel builds graph representations from constraint-based metabolic models and trains a deep learning framework integrating graph structure with gene and metabolite sequences to predict growth-coupled gene deletions, showing accuracy gains of 4-16% over baselines on three models.
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GraphGDel: Constructing and Learning Graph Representations of Genome-Scale Metabolic Models for Growth-Coupled Gene Deletion Prediction
GraphGDel builds graph representations from constraint-based metabolic models and trains a deep learning framework integrating graph structure with gene and metabolite sequences to predict growth-coupled gene deletions, showing accuracy gains of 4-16% over baselines on three models.