All data (derived from three pairs of HCC and non-HCC liver tissues in a dye-swapping approach) were filtered so that 446 up-regulated and 255 down-regulated genes in HCC with at least two-fold difference and p value less than 0.01 were included in the further studies. After removal of the un-annotated genes, a total of 360 and 202 HCC up-and down-regulated genes were subjected to the subsequent gene ontology analyses. The two lists of the differentially expressed genes were analyzed using the online software FatiGo for comparative gene ontology categories including molecular function, biological process and cellular component [29] . They were also imported into the on-line software KEGG2 for pathway mapping [30, 31] . The statistical significance was defined as P < 0.01 between the HCC up-and down-regulated gene groups using Fisher's exact test. From Functional Genomics to Functional Immunomics: New Challenges, Old Problems, Big Rewards The development of DNA microarray technology a decade ago led to the establishment of functional genomics as one of the most active and successful scientific disciplines today. With the ongoing development of immunomic microarray technology—a spatially addressable, large-scale technology for measurement of specific immunological response—the new challenge of functional immunomics is emerging, which bears similarities to but is also significantly different from functional genomics. Immunonic data has been successfully used to identify biological markers involved in autoimmune diseases, allergies, viral infections such as human immunodeficiency virus (HIV), influenza, diabetes, and responses to cancer vaccines. This review intends to provide a coherent vision of this nascent scientific field, and speculate on future research directions. We discuss at some length issues such as epitope prediction, immunomic microarray technology and its applications, and computation and statistical challenges related to functional immunomics. Based on the recent discovery of regulation mechanisms in T cell responses, we envision the use of immunomic microarrays as a tool for advances in systems biology of cellular immune responses, by means of immunomic regulatory network models. During the past decade, the highly successful field of functional genomics experienced huge growth as a result of the development of DNA microarray technology [1] [2] [3] [4] , which made it possible for the first time to measure the RNA expression of thousands of genes in parallel, in a single assay. Immune responses are complex phenomena that supervene on genomics, that is, immune responses ultimately depend on the expression of genes inside a variety of cells, but explaining the function of the immune system only in terms of gene expression in those cells would constitute a reductionist approach. While studying the immune system in terms of genomics is an important goal [5, 6] , the function of the immune system, from antigen processing to epitopespecific immune responses, may be better understood through an integrated approach that takes into account properties of the immune system as a whole.