A Systematic Review - Application of Variants of Rough Sets for Gene Expression Analysis

Abstract

Gene expression analysis plays a pivotal role in understanding disease mechanisms and identifying therapeutic targets, yet its high-dimensional, noisy nature poses significant analytical challenges. Rough Set Theory (RST) and its variants including fuzzy-rough sets, neighborhood rough sets, and hybrid models offer powerful solutions by enabling robust feature selection, classification, and uncertainty management in genomic data. This review explores the application of RST in gene expression analysis, highlighting its advantages over traditional methods like PCA and SVMs, such as superior interpretability, noise tolerance, and the ability to handle missing data. We discuss key methodologies, from reduct-based biomarker discovery to rule-based classification, and present case studies demonstrating their success in cancer subtype prediction and multi-omics integration. Comparative analyses reveal that rough set variants outperform classical approaches in scenarios requiring granular data handling, such as single-cell RNA sequencing and dynamic datasets. Emerging trends, including deep rough neural networks and Explainable AI (XAI) frameworks, are also examined, showcasing their potential to bridge the gap between interpretability and predictive power in precision medicine. Despite challenges in scalability and integration with deep learning, rough sets remain indispensable for extracting biologically meaningful insights from complex genomic data. This review underscores their transformative potential in bioinformatics, advocating for continued innovation to address the evolving demands of high-throughput genomics and personalized healthcare.