<![CDATA[This article offers a thorough analysis of feature selection strategies that use machine learning to analyze gene expression data. In order to extract significant biological insights, the explosion of high-dimensional genomic data has required the invention and use of sophisticated analysis techniques. In this situation, feature selection is essential because it finds the most pertinent genes that have a major impact on the prediction ability of machine learning models. The paper examines a range of feature selection techniques, classifying them into filter, wrapper, and embedding approaches, each having special advantages and disadvantages. The importance of gene expression data in comprehending the molecular mechanisms underlying complicated diseases and biological processes. The difficulties presented by high-dimensional datasets are next explored, with a focus on feature selection as a means of enhancing model interpretability, lowering computational cost, and raising prediction accuracy. In order to shed light on the fundamental ideas and practical uses of well-known feature selection algorithms, the writers thoroughly examine a number of them, including Mutual Information, Relief, and Recursive Feature Elimination (RFE). Additionally, the study assesses these methods' performance critically across a range of datasets and experimental situations, emphasizing important factors like interpretability, scalability, and resilience. The paper also discusses new developments in feature selection, such as the incorporation of deep learning techniques, ensemble methods, and domain expertise. In order to fully realize the promise of gene expression data for biomedical research and clinical applications, the study ends with a discussion of the present issues and prospective future directions in the field. This discussion emphasizes the significance of creating reliable and understandable feature selection techniques. This thorough study will be an invaluable tool for practitioners, researchers, and bioinformaticians in the field of genomics as they navigate the challenging terrain of feature selection techniques in the context of machine learning-based gene expression analysis.]]>
