Parallel Processing of Gene Expression Data to Identify Potential Genes Involved in the Progression of Breast Cancer

Breast Cancer is one of the leading causes of cancer related mortality worldwide, necessitating a deeper understanding of its molecular mechanism. Breast cancer gene expression analysis is essential for identifying key genes involved in breast cancer progression is vital for understanding the disease’s underlying molecular mechanisms and developing targeted therapies. Gene expression analysis, particularly differential expression analysis between cancerous and non-cancerous tissues, is a widely used approach for discovering these key genes. However, large datasets present significant computational challenges. In this study, we implement parallel processing techniques to accelerate the analysis of breast cancer gene expression data. Differential Expression analysis, Fold Change calculation, and paired t-test were performed to identify genes that are significantly over or under expressed. Results are visualized using a volcano plot to highlight crucial genes linked to cancer progression. Parallel processing significantly reduces computation time, making the approach scalable for larger datasets. This work provides a fast and efficient method for identifying potential biomarkers and therapeutic targets in breast cancer research. The proposed approach also provides a potential protocol for other areas of application.