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Alzheimer disease (AD) is characterized by loss of memory and difficulty in learning which can be induced by dysfunction of ion channel genes. In this study, we apply the CTWC algorithm together with SPC method to microarray data measuring ion channel gene expression changes in AD tissue and the control tissue. The basic hypothesis here is that genes with similar expression patters are more likely to exhibit similar functions, and hence appear in same function node of gene ontology. Thus, nodes with more feature genes may have high correlation with disease. Hence, we further analyze relationship between feature ion channel gene module and AD based on GO. The searching process for functional module with more feature ion channel genes is equal to re-select for functional feature based on priori gene function knowledge. The feature ion channel gene module is annotated with the GO functional class it is most enriched with. This can intelligently includes GO information in the clusters and reveal relationship between feature ion channel genes and AD.