Recent advances in genetic feature marker discovery through differential expression and biostatistical analysis

Genetic feature discovery is essential for understanding complex diseases and traits. This comprehensive review provides an in-depth comparison of differential expression analysis methods and statistical hypothesis tests—such as Student’s t-test, Chi-square test, analysis of variance, Empirical Bayes methods, and Significant Analysis of Microarrays—used in genetic feature marker discovery. Our analysis highlights the strengths and weaknesses of these approaches in terms of methodologies, applications, performance, and accuracy. While the statistical tests provide straightforward interpretation, machine learning techniques provide superior capabilities for handling high-dimensional data and complex biological interactions. We conducted two mini-experiments: (i) Identification of differentially expressed genes, upregulated genes and downregulated genes using statistical tools (i.e., Student’s t-test and Welch’s t-test) under different conditions (normalization methods and p-value correction strategies) using the GSE31699 dataset from the NCBI Gene Expression Omnibus, and (ii) gene set enrichment analysis—covering Kyoto Encyclopedia of Genes and Genomes pathways and Gene Ontology terms like Biological process, Cellular component and Molecular function—using the GSE30760 dataset with the DAVID 2021 tool. Furthermore, we discussed the potential of hybrid approaches combining statistical tests with machine learning and optimization techniques for enhanced feature discovery. Future work will focus on multi-omics data integration, the development of explainable AI methods, and scalable algorithms. This review aims to serve as a comprehensive guide for researchers involved in genetic marker identification, highlighting both statistical and computational perspectives on differential expression and gene set enrichment studies.