Gene expression data analysis of Alzheimer's disease based on different brain areas

An improved FastICA (Fast Independent Component Analysis) algorithm using Tukey biweight function as its nonlinear function was proposed to analyze significant genes and regulatory network of multi-brain areas of Alzheimer's disease (AD). To avoid the limitation of traditional clustering methods which group genes in only one class and based on the global similarities in their expression profiles, in this study, the improved biclustering method can identify the significant genes and gene regulatory modules of AD efficiently. According to the function of brain area, this method was applied to the AD brain samples of hippocampus (HIP), entorhinal cortex (EC), media temporal gyrus (MTG) and primary visual cortex respectively which was closely related to human learning and memory. The integrated biological analysis demonstrated that the identified inflammation processes in human brain played an important role in AD.