Mining gene expression regulatory network using independent component analysis

The wide use of DNA microarray technology provide an increasingly detailed view of human transcriptome from hundreds to thousands of genes. The main challenge now is to extract valuable biological information from the colossal amount of data to extract information about pathways and regulatory network underlying the biological processes. In our study, independent component analysis (ICA) is applied to identify significant genes and reconstruct the regulatory networks of Alzheimer's disease (AD) from the arrays of hippocampus and entorhinal cortex of the brain. By integrating the significant genes extracted from different brain regions, the reconstruction of the gene expression regulatory network demonstrated that this method can identify genes and biological modules that play a prominent role in AD and relate the activation patterns of these to AD phenotypes. This report shows that ICA as a microarray data analysis tool could help us to understand the phenotype-pathway relationship and, thus will help us to elucidate the molecular taxonomy of AD.