Year: 2012
Type: article
Abstract: The gene regulatory network (GRN) reveals the regulatory relationships among genes and can provide a systematic understanding of molecular mechanisms underlying biological processes. The importance of computer simulations in understanding cellular processes is now widely accepted; a variety of algorithms have been developed to study these biological networks. The goal of this study is to provide a comprehensive evaluation and a practical guide to aid in choosing statistical methods for constructing large scale GRNs. Using both simulation studies and a real application in E. coli data, we compare different methods in terms of sensitivity and specificity in identifying the true connections and the hub genes, the ease of use, and computational speed. Our results show that these algorithms performed reasonably well, and each method has its own advantages: (1) GeneNet, WGCNA (Weighted Correlation Network Analysis), and ARACNE (Algorithm for the Reconstruction of Accurate Cellular Networks) performed well in constructing the global network structure; (2) GeneNet and SPACE (Sparse PArtial Correlation Estimation) performed well in identifying a few connections with high specificity.
Source: PLoS ONE
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,382
Awards: Collaborative Research: Bayesian Hierarchical Methods for Modeling Chromosomal Spatial Correlation, Predicting Adjuvant Chemotherapy Response in Lung Cancer, Secondary Data Analyses for Substance Abuse Research, North and Central Texas Clinical and Translational Science Initiative (UL1), Targeting Lung Cancer Vulnerabilities, Secondary Data Analyses for Substance Abuse Research
OpenAlex ID: W2061971803