title: A review on the computational approaches for gene regulatory network construction.
abstract: 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.
authors.affiliation: Artificial Intelligence and Bioinformatics Research Group, Faculty of Computing, Universiti Teknologi Malaysia, Skudai, 81310 Johor, Malaysia.
authors.forename: Lian En
authors.initials: LE
authors.lastname: Chai
authors.affiliation: Artificial Intelligence and Bioinformatics Research Group, Faculty of Computing, Universiti Teknologi Malaysia, Skudai, 81310 Johor, Malaysia.
authors.forename: Swee Kuan
authors.initials: SK
authors.lastname: Loh
authors.affiliation: Artificial Intelligence and Bioinformatics Research Group, Faculty of Computing, Universiti Teknologi Malaysia, Skudai, 81310 Johor, Malaysia.
authors.forename: Swee Thing
authors.initials: ST
authors.lastname: Low
authors.affiliation: Artificial Intelligence and Bioinformatics Research Group, Faculty of Computing, Universiti Teknologi Malaysia, Skudai, 81310 Johor, Malaysia. Electronic address: saberi@utm.my.
authors.forename: Mohd Saberi
authors.initials: MS
authors.lastname: Mohamad
authors.affiliation: Artificial Intelligence and Bioinformatics Research Group, Faculty of Computing, Universiti Teknologi Malaysia, Skudai, 81310 Johor, Malaysia.
authors.forename: Safaai
authors.initials: S
authors.lastname: Deris
authors.affiliation: Artificial Intelligence and Bioinformatics Research Group, Faculty of Computing, Universiti Teknologi Malaysia, Skudai, 81310 Johor, Malaysia.
authors.forename: Zalmiyah
authors.initials: Z
authors.lastname: Zakaria
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D019295:Computational Biology / Q000379:methods*; D053263:Gene Regulatory Networks; D016571:Neural Networks, Computer
keywords: Bayesian network; Boolean network; Computational approaches; Dynamic Bayesian network; Gene expression data; Gene regulatory network; Neural network; Ordinary differential equation; Probabilistic Boolean network