Cart (Loading....) | Create Account
Close category search window
 

GNAnalyzer: A Novel System for Analyzing Gene Networks from Microarray Data with Bayesian Networks

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Chen, A.H. ; Dept. of Med. Inf., Tzu Chi Univ., Hualien, Taiwan ; Ching-Heng Lin ; Guan-Ting Chen ; Jen-Chieh Hsieh

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. 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. 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.

Published in:

Computer Science and Information Engineering, 2009 WRI World Congress on  (Volume:5 )

Date of Conference:

March 31 2009-April 2 2009

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.