A comparative review of recent bioinformatics tools for inferring gene regulatory networks using time-series expression data

Abstract

The Gene Regulatory Network (GRN) inference problem in computational biology is challenging. Many algorithmic and statistical approaches have been developed to computationally reverse engineer biological systems. However, there are no known bioinformatics tools capable of performing perfect GRN inference. Here, we review and compare seven recent bioinformatics tools for inferring GRNs from time-series gene expression data. Standard performance metrics for these seven tools based on both simulated and experimental data sets are generally low, suggesting that further efforts are needed to develop more reliable network inference tools.