Peer Review Report For: A Novel Particle Marine Predator Optimizer for Gene Selection Health Problem [version 1; peer review: 1 approved with reservations, 1 not approved]

Background High-dimensional microarray data complicates reliable cancer classification. Compact, informative gene panels are needed to maintain predictive power while improving interpretability and cost. Methods We propose a two-stage feature-selection pipeline. Stage 1 ranks genes via an ensemble of filters—ReliefF, chi-square, and Kullback–Leibler divergence—tempered by minimum redundancy–maximum relevance to promote diversity. Stage 2 performs wrapper-based subset search using a Particle Marine Predator Optimizer that fuses Marine Predators Algorithm for global exploration with Particle Swarm Optimization for local refinement. The objective maximizes cross-validated SVM accuracy while penalizing subset size. Results Across seven benchmarks (Breast, CNS, Leukemia, Leukemia-3c, Leukemia-4c, Lymphoma, Ovarian), we compare against Bat Algorithm, Grey Wolf Optimizer, Marine Predators Algorithm, White Shark Optimizer, and recent representatives using accuracy, F1, precision, sensitivity, Matthews correlation coefficient, selected-gene count, and convergence behavior. The method frequently matches or exceeds alternatives while selecting few genes, achieving perfect accuracy on several datasets (Leukemia, Leukemia-3c, Lymphoma, Ovarian) and stable, strong performance on the remainder. Typical subset sizes are 2–5 genes for Leukemia variants, 7–8 for CNS, and ~20 for Breast. Optimization traces show rapid, steady improvement. Conclusions The pipeline achieves an effective exploration–exploitation balance, yielding compact gene panels without sacrificing classification performance. Its modular design supports straightforward extension to larger cohorts and other omics modalities.