Discovery of gene network variability across samples representing multiple classes Online publication date: Mon, 11-Oct-2010
by Younhee Ko, ChengXiang Zhai, Sandra L. Rodriguez-Zas
International Journal of Bioinformatics Research and Applications (IJBRA), Vol. 6, No. 4, 2010
Abstract: Gene networks have been predicted using the expression profiles from microarray experiments that include multiple samples representing each of several classes or states (e.g., treatments, developmental stages, health status). A framework that integrates Bayesian networks, mixture of gene co-expression models and clustering is proposed to further mine information from the variation of samples within and across classes and enhance the understanding of gene networks. The approach was evaluated on two independent pathways using data from two microarray experiments. Our algorithm succeeded on reconstructing the topology of the gene pathways when benchmarked against empirical reports and randomised data sets. The majority or all the samples within a class shared the same co-expression model and were classified within the corresponding class. Our approach uncovered both gene relationships and profiles that are unique to a particular class or shared across classes.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Bioinformatics Research and Applications (IJBRA):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook