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

Authors: Kevin Byron; Jason T.L. Wang

Addresses: Department of Computer Science, New Jersey Institute of Technology, Newark, NJ 07102, USA ' Department of Computer Science, New Jersey Institute of Technology, Newark, NJ 07102, USA

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.

Keywords: DREAM; dialogue for reverse engineering assessments and methods ESCAPE; embryonic stem cell atlas from pluripotency evidence GRN; gene regulatory network; reverse engineering; time-series.

DOI: 10.1504/IJDMB.2018.094889

International Journal of Data Mining and Bioinformatics, 2018 Vol.20 No.4, pp.320 - 340

Received: 26 Nov 2017
Accepted: 06 Jul 2018

Published online: 25 Sep 2018 *

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