AccScience Publishing / AIH / Online First / DOI: 10.36922/AIH025180036
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REVIEW ARTICLE

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

Ankita Saha1,2 ,  Shibakali Gupta3 ,  Chyan Paul4 ,  Saurav Mallik5,6* ,  Korhan Cengiz7*
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1 Department of Computer Science, Swami Vivekananda University, Barrackpore, West Bengal, India
2 Department of Science and Management, ABS Academy of Management and Health Science, Durgapur, West Bengal, India
3 Department of Computer Science and Engineering, University Institute of Technology, Burdwan University, West Bengal, India
4 Department of Computer Science and Engineering, Swami Vivekananda University, Barrackpore, West Bengal, India
5 Department of Biostatistics, University of Miami, Florida, United States of America
6 College of Pharmacy, University of Arizona, Tucson, Arizona, United States of America
7 Department of Electrical Engineering, Prince Mohammad Bin Fahd University, Al Khobar, Saudi Arabia
AIH, 025180036 https://doi.org/10.36922/AIH025180036
Received: 28 April 2025 | Revised: 17 July 2025 | Accepted: 1 August 2025 | Published online: 9 September 2025
© 2025 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

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.

Keywords
Genetic feature discovery
Statistical tests
KEGG pathway analysis
Gene set enrichment analysis
Funding
None.
Conflict of interest
The authors declare that they have no competing interests.
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