We concentrated on in-silico analysis in this study, and we utilised R and Python, two well-known bioinformatics tools, to do this. The development of these tools is focused on achieving a deeper understanding of the patterns of gene expression as well as the ways in which genes that are implicated in Low Grade Serous Ovarian Cancer interact with one another. Actions to Take and Resources: These microarray datasets were obtained from NCBI OMNIBUS, then normalised, and finally processed before being used in this work. After doing pathway analysis, it was concluded that RPS27L was the best co-expression gene out of the fifteen new hub genes. This was further supported by the fact that it had the highest network topology score. On the basis of the mean values of degree, closeness, and betweenness, the hub genes were sorted in order of consideration. Following that, the top ten new hub genes were classified according to the scores that were higher. Conclusion. By comparing the nine different pathways that were generated by Cluego, we were able to determine which one was the most effective. The objective of this study was to find certain biomarkers for ovarian cancer that may be taken into consideration throughout the process of discovering structurally based medications.
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