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A set of microarray images were acquired by a sequence of biological experiments which were scanned via a high resolution scanner. For each spot corresponding to a gene, the ratio of Cy3 and Cy5 fluorescent signal intensities was obtained and which may be normalied based on piecewise linear regression such as lowess method. In this study, we computed from 55 microarray images to get an M × N genematrix, A, with N = 55 patients and M = 13574 effected genes in each microarray. We start with our gene discovery from a genematrix A epsillon R^M×N, M = 13574, N = 55, including N1 = 29 patients of hepatitis B virus (HBV), N2 = 21 patients of hepatitis C virus (HCV), 1 patient clinically diagnosed to be infected with HCV as well as HBV, and 4 patients were neither HCV nor HBV infected. Simple software was developed to solve the following problems: (i) Detect differentially expressed genes and (ii) Select a subset of genes which best distinguishes HBV patients from HCV ones.