BIMS-GA: Adaptive Multi-Objective Optimization for Network-Aware Pancreatic Ductal Adenocarcinoma Biomarker Discovery

Pancreatic ductal adenocarcinoma (PDAC) has a five-year relative survival of 8%. Feature selection optimizes accuracy, survival concordance, and network structure separately; whether uniting them matters has not been determined. The BIMS-GA optimizes the cross-validated AUC, Cox concordance, and STRING v11.0 protein-protein interaction coherence in one fitness function and was compared against fourteen alternative methods on GSE62452 (n = 130) and eight external evaluation sets. Using a nested protocol, the fifteen methods had pooled out-of-fold AUCs spanning 0.877-0.900; the BIMS-GA ranked thirteenth with 0.8855. No DeLong comparisons survived correction. Stability did not persist either: the ten-seed Jaccard index of 0.804 exceeded every comparator but fell to 0.521 in a second seed block. Network coherence separated them by two orders of magnitude, 0.480 against 0.005-0.192, in an ordering unbroken across twenty runs. Removing the network term raised the discovery AUC by 0.0117 and lowered external prognostic concordance by 0.0149 in all ten runs, acting as a regularizer. The twenty-gene panel comprises one laminin-332 and an integrin adhesion program with no isolated member, stratifying survival in four cohorts (hazard ratios of 2.92-4.13 fitted within each cohort; 1.02-2.17 transferred). Network coherence, not discrimination, is the axis that remained separate in every run examined, while short-run stability did not.