Hybrid Gene Selection Algorithm for Cancer Classification Using Nuclear Reaction Optimization (NRO)

• Published in: Current issues in molecular biology Vol. 47; no. 9; p. 683
• Main Authors: Alkamli, Shahad, Alshamlan, Hala
• Format: Journal Article
• Language: English
• Published: 25.08.2025
• ISSN: 1467-3045; 1467-3045
• DOI: 10.3390/cimb47090683

Microarray gene expression data are characterized by high dimensionality and small sample sizes, which complicates cancer classification tasks. To address these challenges, this study proposes a hybrid gene selection approach that integrates a filter-based dimensionality reduction method with a metaheuristic optimizer. Specifically, the method applies the F-score statistical filter to rank and reduce gene features, followed by Nuclear Reaction Optimization (NRO) to refine the selection. This combination is referred to as the F-score-based Nuclear Reaction Optimization method or F-NRO. The performance of F-NRO was evaluated on six publicly available microarray cancer datasets (Colon, Leukemia1, Leukemia2, Lung, Lymphoma, and SRBCT) using Support Vector Machines (SVMs) and Leave-One-Out Cross-Validation (LOOCV). Comparative analysis against several existing hybrid gene selection algorithms demonstrates that F-NRO achieves high classification accuracy, including perfect accuracy on five datasets, using compact gene subsets. These results suggest that F-NRO is an effective and interpretable solution for gene selection in cancer classification tasks.