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

Kevin Byron, Jason T.L. Wang

Research output: Contribution to journalReview articlepeer-review

3 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)320-340
Number of pages21
JournalInternational Journal of Data Mining and Bioinformatics
Volume20
Issue number4
DOIs
StatePublished - 2018

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Biochemistry, Genetics and Molecular Biology(all)
  • Library and Information Sciences

Keywords

  • DREAM
  • Dialogue for reverse engineering assessments
  • Embryonic stem cell atlas from pluripotency evidence GRN
  • Gene regulatory network
  • Methods ESCAPE
  • Reverse engineering
  • Time-series

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