Discovery of gene network variability across samples representing multiple classes

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)402-417
Number of pages16
JournalInternational Journal of Bioinformatics Research and Applications
Volume6
Issue number4
DOIs
StatePublished - Oct 2010

Keywords

  • Bayesian network
  • Circadian rhythm pathway
  • Gene expression and co-expression models
  • Gene pathway
  • Starch and sucrose metabolism pathway

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Clinical Biochemistry
  • Health Information Management

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