Los Angeles, California USA
Mar. 31, 2009 to Apr. 2, 2009
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CSIE.2009.515
Background: Since the development of biotechnologies such as array-based hybridization, massive amounts of gene expression profiles are quickly accumulating. How to utilize these huge amounts of data 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. Results: In this paper, we have developed a novel system to construct and analyze various gene networks from microarray datasets. Three aspects characterize the major contributions of this paper. (1) Five Bayesian network algorithm codes were developed and written to construct gene networks of the yeast cell cycle using the information from four different microarray datasets. (2) A gene network analyzing system, GNAnalyzer, consisting of several user-friendly interfaces was implemented. GNAnalyzer is capable of running Bayesian algorithms, constructing gene networks, and analyzing the performance of each network algorithm simultaneously. (3) The system utilizes both the powerful processing ability of MatLab and the dynamic interfaces of LabVIEW in a single platform. Conclusions: This is the first time of this kind of design to be applied in bioinformatics. The system is designed to be extendible. Our next goal is to apply this technique to other real biomedical applications, such as human cancer classification and prognostic prediction.
Austin H. Chen, Ching-Heng Lin, Guan-Ting Chen, Jen-Chieh Hsieh, "GNAnalyzer: A Novel System for Analyzing Gene Networks from Microarray Data with Bayesian Networks", CSIE, 2009, Computer Science and Information Engineering, World Congress on, Computer Science and Information Engineering, World Congress on 2009, pp. 22-27, doi:10.1109/CSIE.2009.515