Biological significance of a novel biclustering technique on genetic expression data
Unlocking the complexity of a living organism's biological processes, functions, and genetic network is vital in learning how to improve the health of humankind. Genetic analysis, especially biclustering, is a significant step in this process. Learning how genes function together in groups, how they regulate under different conditions, and what proteins created by genes trigger certain biological processes will pave new pathways for genetic engineering and disease therapy. This experiment analyzes one such biclustering technique that combs over a gene expression matrix to find subgroups of genes with coherent evolution, that is genes whose expression levels stay constant or increase coherently over a subgroup of conditions or samples. This biclustering technique was applied to an S. cervisiae, or yeast, genetic expression data matrix. The biclustering was again performed on the same initial matrix two more times, each with selected genes omitted with the intent to increase the biological correlation relationship of the genes in the resultant biclusters. The final results were analyzed using a categorized system of known protein functional annotation from the yeast genome to find the biological significance of the submatrices detected by the algorithm