Open Access
Open access
Genes, volume 15, issue 12, pages 1530

LBF-MI: Limited Boolean Functions and Mutual Information to Infer a Gene Regulatory Network from Time-Series Gene Expression Data

Shohag Barman 1
Fahmid Al Farid 2
Hira Lal Gope 3
Md. Ferdous Bin Hafiz 4
Niaz Ashraf Khan 5
Sabbir Ahmad 6
Sarina Mansor 2
1
 
Department of Computer Science and Engineering, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Pirojpur 8500, Bangladesh
4
 
Department of Computer Science and Engineering, Southeast University, Dhaka 1208, Bangladesh
5
 
Department of Computer Science and Engineering, University of Liberal Arts Bangladesh, Dhaka 1207, Bangladesh
Publication typeJournal Article
Publication date2024-11-27
Journal: Genes
scimago Q2
wos Q2
SJR0.817
CiteScore5.2
Impact factor2.8
ISSN20734425
Abstract

Background: In the realm of system biology, it is a challenging endeavor to infer a gene regulatory network from time-series gene expression data. Numerous Boolean network inference techniques have emerged for reconstructing a gene regulatory network from a time-series gene expression dataset. However, most of these techniques pose scalability concerns given their capability to consider only two to three regulatory genes over a specific target gene. Methods: To overcome this limitation, a novel inference method, LBF-MI, has been proposed in this research. This two-phase method utilizes limited Boolean functions and multivariate mutual information to reconstruct a Boolean gene regulatory network from time-series gene expression data. Initially, Boolean functions are applied to determine the optimum solutions. In case of failure, multivariate mutual information is applied to obtain the optimum solutions. Results: This research conducted a performance-comparison experiment between LBF-MI and three other methods: mutual information-based Boolean network inference, context likelihood relatedness, and relevance network. When examined on artificial as well as real-time-series gene expression data, the outcomes exhibited that the proposed LBF-MI method outperformed mutual information-based Boolean network inference, context likelihood relatedness, and relevance network on artificial datasets, and two real Escherichia coli datasets (E. coli gene regulatory network, and SOS response of E. coli regulatory network). Conclusions: LBF-MI’s superior performance in gene regulatory network inference enables researchers to uncover the regulatory mechanisms and cellular behaviors of various organisms.

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