The advent of DNA microarray datasets has revolutionized both machine learning (ML) and healthcare by providing critical insights into variations in gene expression across different tissue and cell samples. These datasets, which typically contain thousands to tens of thousands of features, are instrumental in aiding disease diagnosis and classification. However, many genes within these datasets are irrelevant or noisy, making classification tasks challenging due to the high dimensionality of the data and the limited sample sizes. Feature selection is a crucial strategy to mitigate these challenges, as it helps identify the most relevant genes while discarding the irrelevant ones. This process not only improves the performance of ML models but also plays a pivotal role in accurately classifying tumor types and refining treatment strategies. This chapter reviews recent advancements in feature selection methods specifically tailored for microarray data analysis. It highlights the common challenges, including data imbalance, complexity, and missing values, which can hinder the classification process. Additionally, it provides an empirical analysis of various feature selection techniques applied to representative datasets, offering a comparative perspective rather than identifying a single “best” method. Importantly, the interpretation of gene signatures resulting from feature selection also has potential applications in computational social science, such as identifying key factors influencing public health, social behaviors, and policy decisions.