Identifying Prostate Cancer-Related Networks from Microarray Data Based on Genotype-Phenotype Networks Using Markov Blanket Search

Abstract

The identification of significant disease-related genes and networks is an important issue in understanding underlying mechanisms of cells. We integrate phenotype networks, protein networks and efficiently utilize gene expression data to identify human disease networks. We use prostate cancer data as our test domain. In comparison with statistical methods such as t-test and Wilcoxon test, our method identifies more prostate cancer-related genes reported in published database and literature. Interleukin-type growth factors, Ras related oncogenes and cytokine interactions canonical pathways are found to be significantly related to prostate cancer.