A review on the computational approaches for gene regulatory network construction

Lian En Chai, Swee Kuan Loh, Swee Thing Low, Mohd Saberi Mohamad, Safaai Deris, Zalmiyah Zakaria

Research output: Contribution to journalReview articlepeer-review

171 Citations (Scopus)


Many biological research areas such as drug design require gene regulatory networks to provide clear insight and understanding of the cellular process in living cells. This is because interactions among the genes and their products play an important role in many molecular processes. A gene regulatory network can act as a blueprint for the researchers to observe the relationships among genes. Due to its importance, several computational approaches have been proposed to infer gene regulatory networks from gene expression data. In this review, six inference approaches are discussed: Boolean network, probabilistic Boolean network, ordinary differential equation, neural network, Bayesian network, and dynamic Bayesian network. These approaches are discussed in terms of introduction, methodology and recent applications of these approaches in gene regulatory network construction. These approaches are also compared in the discussion section. Furthermore, the strengths and weaknesses of these computational approaches are described.

Original languageEnglish
Pages (from-to)55-65
Number of pages11
JournalComputers in Biology and Medicine
Issue number1
Publication statusPublished - May 1 2014
Externally publishedYes


  • Bayesian network
  • Boolean network
  • Computational approaches
  • Dynamic Bayesian network
  • Gene expression data
  • Gene regulatory network
  • Neural network
  • Ordinary differential equation
  • Probabilistic Boolean network

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics


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