Abstract
Gene expression profiles could be a powerful tool for therapeutic decisions and prognostic estimation of disease. It is useful but transforming such huge data into a mechanistic understanding of disease is very difficult. Recently, researches aimed at uncorvering the modular organization and function of transcriptional networks and responses in disease. In this study, such modules[1] are extracted from the gene expression profiles as statistically independent components(ICs) obtained by independent component analysis(ICA). Therefore, a probit model for classificaion of disease is constructed by the ICs, and the model parameters are estimated by Markov Chain Monte Carlo(MCMC) method. The present method is applied to gene expression profiles of glioblastoma and anaplastic oligodendroglioma. As a result, we obtain a probit model which can classify the two kinds of gliomas. In addition, biological functions are extracted as the ICs.
