Authors: Kabir Suryavanshi
Abstract: Gene expression analysis is a cornerstone of modern molecular biology, enabling the elucidation of gene functions, regulatory mechanisms, and disease associations. With the exponential growth of high-throughput sequencing technologies, such as microarrays and RNA-Seq, vast datasets are now available, presenting both an unprecedented opportunity and a computational challenge. Bioinformatics algorithms have risen to meet these demands by providing accessible and scalable methods for data normalization, feature selection, clustering, classification, and pathway analysis. This review presents a comprehensive overview of the key algorithms used in gene expression analysis, discussing their theoretical foundations, practical applicability, and comparative strengths. Emphasis is placed on the transition from traditional statistical methods to contemporary machine learning approaches, highlighting how each has contributed to unraveling complex biological phenomena. Emerging issues, such as data heterogeneity, batch effects, and the integration of multi-omics datasets, are examined alongside the innovative algorithmic solutions developed to tackle them. Furthermore, the impact of algorithmic advances on translational research, including biomarker discovery, drug development, and personalized medicine, is discussed. By critiquing the evolution of bioinformatics tools and their roles in gene expression analysis, this article aims to guide researchers in selecting and applying the most appropriate algorithms for their specific investigative goals, while also identifying areas for future development. Ultimately, as biological research grows increasingly data-driven, the synergy between algorithm development and gene expression analysis will continue to deepen our understanding of functional genomics and disease etiology.
DOI: https://doi.org/10.5281/zenodo.16981825
