Impact of Class Imbalance Correction on Biological Feature Selection in Pancreatic Ductal Adenocarcinoma Methylation Data

Class imbalance is common in biomedical machine learning, yet imbalance-correction strategies are typically evaluated using predictive performance rather than their effects on biological interpretation. We evaluated six resampling methods: Random Oversampling (ROS), Synthetic Minority Oversampling Technique (SMOTE), Adaptive Synthetic Sampling (ADASYN), Random Undersampling (RUS), ClusterCentroids, and AllKNN (All K-Nearest Neighbors), together with a no-resampling baseline, using DNA methylation data from The Cancer Genome Atlas Pancreatic Ductal Adenocarcinoma (PDAC) cohort. The analysis included 485,578 CpG markers of which 406,807 were retained after quality-control filtering. A continuous feature-ranking framework merging Random Forest importance and ANOVA F-statistics was applied across repeated runs. Biological relevance was evaluated through hypergeometric enrichment against a curated PDAC gene set and preranked Gene Set Enrichment Analysis (GSEA) using 309 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. All seven analysis strategies produced significant enrichment for literature-supported pancreatic cancer-associated genes, with hypergeometric p-values ranging from 3.34×10−16 to 1.28×10−7. SMOTE recovered the largest absolute number of PDAC-associated genes (141 of 13,827 selected genes), whereas RUS achieved the greatest fold enrichment (2.17-fold; 88 observed versus 40.61 expected). Notably, the no-resampling baseline remained highly competitive, recovering 112 PDAC-associated genes with 1.86-fold enrichment and a p-value of 5.27×10−14, indicating that resampling was not necessary to recover biologically relevant features under the evaluated feature-ranking framework. Across methods, 169–234 of the 309 tested KEGG pathways were significant at false discovery rate ≤0.05. ClusterCentroids produced the largest number of significant pathways (234), followed by the no-resampling baseline (225) and RUS (223). Seven to nine predefined PDAC-focused pathways were recovered per method, with RUS and ClusterCentroids recovering all nine. Core pathways consistently identified across all methods included MAPK, PI3K–Akt, TGF-β, Notch, Hedgehog, focal adhesion, and cell-cycle signaling. These findings show that resampling strategies produce distinct trade-offs between biomarker coverage, biological specificity, and pathway breadth, supporting biologically informed evaluation rather than selection of a single universally optimal imbalance-correction method.