A comparative review of recent bioinformatics tools for inferring Gene Regulatory Networks using time-series expression data

Authors

Kevin Byron, Department of Computer Science
Jason T.L. Wang, Department of Computer Science

Document Type

Article

Publication Date

1-1-2018

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.

Identifier

85054148023 (Scopus)

Publication Title

International Journal of Data Mining and Bioinformatics

External Full Text Location

https://doi.org/10.1504/ijdmb.2018.10016321

e-ISSN

17485681

ISSN

17485673

First Page

320

Last Page

340

Issue

4

Volume

20

Recommended Citation

Byron, Kevin and Wang, Jason T.L., "A comparative review of recent bioinformatics tools for inferring Gene Regulatory Networks using time-series expression data" (2018). Faculty Publications. 8949.
https://digitalcommons.njit.edu/fac_pubs/8949