Table · dataset · 2026

<p>Subtype-classification performance (AUC) across conditions for basal-like and classical pancreatic ductal adenocarcinoma using a single global graph across patients using degree based module scores from Moffitt genes. The absolute correlation threshold was set to 0.2 to measure performance with the maximum number of valid degrees. In turn, retaining a larger number of lower-weight edges altered the degree-weighted scores among patients, whereas limiting the number of retained edges could reduce score variance across graph conditions because the graphs differed in their edge-weight distributions and edge counts. For the global-graph analysis, lowering the threshold allowed us to evaluate how heterogeneous connectivity and increased variation in degree-weighted patient scores affected model performance. All other experiments set the edge correlation threshold to 0.5 to isolate strongly correlated subsets of genes.</p>

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<p>Subtype-classification performance (AUC) across conditions for basal-like and classical pancreatic ductal adenocarcinoma using a single global graph across patients using degree based module scores from Moffitt genes.