Application of Euclidean distance measurement and principal component analysis for gene identification

Gene systems are extremely complex, heterogeneous, and noisy in nature. Many statistical tools which are used to extract relevant feature from genes provide fuzzy and ambiguous information. High-dimensional gene expression database available in public domain usually contains thousands of genes. Efficient prediction method is demanding nowadays for accurate identification of such database. Euclidean distance measurement and principal component analysis methods are applied on such databases to identify the genes. In both methods, prediction algorithm is based on homology search approach. Digital Signal Processing technique along with statistical method is used for analysis of genes in both cases. A two-level decision logic is used for gene classification as healthy or cancerous. This binary logic minimizes the prediction error and improves prediction accuracy. Superiority of the method is judged by receiver operating characteristic curve.