Ensemble-based network aggregation improves the accuracy of gene network reconstruction

Rui Zhong, Jeffrey D. Allen, Guanghua Xiao, Yang Xie

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

Reverse engineering approaches to constructing gene regulatory networks (GRNs) based on genome-wide mRNA expression data have led to significant biological findings, such as the discovery of novel drug targets. However, the reliability of the reconstructed GRNs needs to be improved. Here, we propose an ensemble-based network aggregation approach to improving the accuracy of network topologies constructed from mRNA expression data. To evaluate the performances of different approaches, we created dozens of simulated networks from combinations of gene-set sizes and sample sizes and also tested our methods on three Escherichia coli datasets. We demonstrate that the ensemble-based network aggregation approach can be used to effectively integrate GRNs constructed from different studies - producing more accurate networks. We also apply this approach to building a network from epithelial mesenchymal transition (EMT) signature microarray data and identify hub genes that might be potential drug targets. The R code used to perform all of the analyses is available in an R package entitled ''ENA'', accessible on CRAN (http://cran.r-project.org/web/packages/ENA/).

Original languageEnglish (US)
Article numbere106319
JournalPLoS One
Volume9
Issue number11
DOIs
StatePublished - Nov 12 2014

Fingerprint

Gene Regulatory Networks
Agglomeration
Genes
drugs
topology
Messenger RNA
Epithelial-Mesenchymal Transition
engineering
Drug Discovery
genes
Sample Size
Escherichia coli
genome
Reverse engineering
Genome
Microarrays
Pharmaceutical Preparations
gene regulatory networks
Topology
sampling

ASJC Scopus subject areas

  • Agricultural and Biological Sciences(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Medicine(all)

Cite this

Ensemble-based network aggregation improves the accuracy of gene network reconstruction. / Zhong, Rui; Allen, Jeffrey D.; Xiao, Guanghua; Xie, Yang.

In: PLoS One, Vol. 9, No. 11, e106319, 12.11.2014.

Research output: Contribution to journalArticle

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