Optimizing TCGA Data Analysis: Unveiling Crucial Cancer-Related Gene Alterations Through a Fusion Approach QL Gradient

Analyzing The Cancer Genome Atlas (TCGA) data helps researchers identify genes frequently mutated or altered in cancer. These alterations may play a crucial role in the development and progression of cancer. The Cancer Genome Atlas (TCGA) data allows classifying cancers into molecular subtypes based on their genomic profiles. By identifying specific genetic alterations in cancer cells, researchers can explore targeted therapies that aim to inhibit the activity of these altered genes. Early systems used popular tools such as edgeR and DESeq2 for genetic analysis, but these tools are sensitive to the experimental design, and incorrect model specification can lead to biased results. In the latest research, people have applied clustering algorithms to group samples based on their gene expression patterns. Different clustering algorithms have various parameters that need to be set, and the choice of these parameters can influence the clustering results. Optimal parameter tuning may require expertise and exploration. The proposed research applies a genetic optimization algorithm for classifying TCGA by integrating quantum and gradient boosting.