Deciphering Gene Patterns Through Gene Selection Using SARS-CoV Microarray Data

Severe acute respiratory syndrome coronavirus type 1 (SARS-CoV-1) outbreak has presented a serious danger to world health, and in subsequent years, SARS-CoV-2 emerged, demanding a detailed analysis of its genetic pattern. This work used gene selection analysis to understand how genes respond to SARS-CoV-1 infection. The objective was to identify key genes that play critical roles in virus-host interaction and potentially serve as targets for therapeutic interventions. Information on gene expression from the Gene Expression Omnibus dataset GSE1739 was used to achieve this objective. This research performed gene selection using a hybrid of multi-objective cuckoo search with evolutionary operators to narrow down the set of genes that demonstrated significant expression changes. Next, the chosen genes were functionally annotated to identify their contributions to viral pathogenesis and responses. The findings of our investigation shed insight into important biochemical pathways and cellular processes impacted by the virus by identifying a group of important genes that are differently expressed during SARS-CoV-1 infection. Overall, the thorough investigation of the GSE130967 data revealed insightful information about the interactions between SARS-CoV-1 and its host, laying a foundation for further research to combat emerging coronavirus infections and improve global public health preparedness.