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Identifying disease-relevant genes and functional modules, based on gene expression pro- files and gene functional knowledge, is of high im- portance for studying disease mechanisms and sub- typing disease phenotypes. Using gene categories of biological process and cellular component in Gene Ontology, we propose an approach to selecting func- tional modules enriched with differentially expressed genes, and identifying the feature functional modules of high disease discriminating abilities. Using the differentially expressed genes in each feature module as the feature genes, we reveal the relevance of the modules to the studied diseases. Using three data- sets for prostate cancer, gastric cancer, and leukemia, we have demonstrated that the proposed modular approach is of high power in identifying functionally integrated feature gene subsets that are highly rele- vant to the disease mechanisms. Our analysis has also shown that the critical disease-relevant genes might be better recognized from the gene regulation network, which is constructed using the characterized functional modules, giving important clues to the concerted mechanisms of the modules responding to complex disease states. In addition, the proposed approach to selecting the disease-relevant genes byjointly considering the gene functional knowledge suggests a new way for precisely classifying disease samples with clear biological interpretations, which is critical for the clinical diagnosis and the elucidation of the pathogenic basis of complex diseases.
Identifying disease-relevant genes and functional modules, based on gene expression pro-files and gene functional knowledge, is of high im- portance for studying disease mechanisms and sub- typing disease phenotypes. Using gene categories of biological process and cellular component in Gene Ontology , we propose an approach to selecting func- tional modules enriched with differentially expressed genes, and identifying the feature functional modules of high disease discriminating abilities. Using the differentially expressed genes in each feature module as the feature genes, we reveal the relevance of the modules to the healing diseases. Using three data-sets for prostate cancer, gastric cancer, and leukemia, we have demonstrated that the proposed modular approach is high power in identifying functionally integrated feature genes subsets that are highly rele- vant to the disease mechanisms. Our analysis has also shown that the critical disease-relevant genes might be better recognize d from the gene regulation network, which is constructed using the characterized functional modules, giving important clues to the concerted mechanisms of the modules responding to complex disease states. In addition, the proposed approach to selecting the disease-relevant genes byjointly considering the gene functional knowledge suggests a new way for precisely classifying disease samples with clear biological interpretations, which is critical for the clinical diagnosis and the elucidation of the pathogenic basis of complex diseases.