ANALYSIS OF THE CURRENT STATE OF METHODS FOR SUBSET FORMATION OF SIGNIFICANT AND MUTUALLY EXPRESSED GENE EXPRESSION DATA
DOI:
https://doi.org/10.32782/mathematical-modelling/2024-7-2-2Keywords:
gene expression, diagnostic system, gene ontology, Shannon entropy, statistical criteriaAbstract
The article provides a comprehensive analysis of the modern approaches to forming subsets of significant and mutually expressed genes based on gene expression data obtained through DNA microarray and RNA sequencing technologies. This topic is of particular relevance since the high-dimensional gene expression matrices generated in such studies require effective preprocessing to identify genes that are critical for understanding the biological systems’ conditions. Modern clustering and biclustering methods play a vital role in reducing the number of genes for further analysis, thereby improving the accuracy of diagnostics and biological process analysis. Moreover, the use of Gene Ontology (GO) facilitates the structured description of the functional roles of genes in various biological processes, molecular functions, and cellular components. This enhances data processing quality and enables researchers to focus on key genes playing significant roles in pathological processes. The article also addresses different stages of data preprocessing, including the removal of non-expressed genes, the identification of differentially expressed genes using tools like DESeq2 and EdgeR, and the application of meta-analysis to integrate results from multiple studies. GO analysis allows researchers to effectively identify enriched GO terms associated with functionally significant genes and interpret the results through visualizations such as graphs and diagrams. However, one of the key challenges remains the standardization of results and ensuring consistency across various research groups, which is essential for integrating data into a unified diagnostic system. The paper highlights the importance of further enhancing approaches to gene expression analysis and data integration, which will significantly improve the efficiency of bioinformatics research in disease diagnostics and personalized medicine.
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