Article: Discovery of gene network variability across samples representing multiple classes Journal: International Journal of Bioinformatics Research and Applications (IJBRA) 2010 Vol.6 No.4 pp.402 - 417 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. Inderscience Publishers - linking academia, business and industry through research

Title: Discovery of gene network variability across samples representing multiple classes

Authors: Younhee Ko, ChengXiang Zhai, Sandra L. Rodriguez-Zas

Addresses: Department of Computer Science, University of Illinois at Urbana-Champaign, 201 N. Goodwin Ave., Urbana, IL 61801, USA. ' Department of Computer Science, Institute for Genomic Biology, Graduate School of Library and Information Science, and Department of Statistics, University of Illinois at Urbana-Champaign 201 N. Goodwin Ave., Urbana, IL 61801, USA. ' Department of Animal Sciences, Department of Statistics, Institute for Genomic Biology University of Illinois at Urbana-Champaign, 1207 W. Gregory Dr., Urbana, IL 61801, USA

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.

Keywords: gene pathways; Bayesian networks; starch metabolism pathway; sucrose metabolism pathway; circadian rhythm pathway; gene expression models; gene co-expression models; gene networks; microarray data; bioinformatics; clustering.

DOI: 10.1504/IJBRA.2010.036002

International Journal of Bioinformatics Research and Applications, 2010 Vol.6 No.4, pp.402 - 417

Received: 10 Jun 2009
Accepted: 14 Dec 2009
Published online: 11 Oct 2010 *

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Title: Discovery of gene network variability across samples representing multiple classes

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