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/).
For example, in REF , an Ensemble-based Network Aggregation method (ENA) is proposed to integrate gene networks derived from different methods and datasets, in order to improve the accuracy of network inference.
Ensemble-Based Network Aggregation Improves the Accuracy of Gene Network Reconstruction
PLoS ONE
PLoS ONE
Biology Computer Science Medicine