An Intelligent System for Analyzing Gene Networks from Microarray Data

Abstract

High-throughput techniques, such as microarray experiments, have given biologists the opportunity to measure the expression levels of a huge amount of genes at the same time. How to utilize these huge amounts of data, however, has become a major challenge in the post-genomic research era. One approach utilizes a Bayesian network, a graphical model that has been applied toward inferring genetic regulatory networks from microarray experiments. However, a user-friendly system that can display and analyze various gene networks from microarray experimental datasets is now needed. In this paper, we developed a novel system for constructing and analyzing gene networks. Firstly, we developed five Bayesian network algorithms to construct gene networks of the yeast cell cycle from four different microarray datasets. Secondly, we implemented a user-friendly gene network analyzing system. GNAnalyzer is capable of generating gene networks of the yeast cell cycle from experimental microarray data but also analyzing the performance of gene networks for every algorithm. Thirdly, our system utilizes both the powerful processing abilities of MatLab and the dynamic interface of LabVIEW in a single platform.