A novel constrained genetic algorithm-based Boolean network inference method from steady-state gene expression data | Zendy

Hung-Cuong Trinh | Zendy; YungKeun Kwon | Zendy

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A novel constrained genetic algorithm-based Boolean network inference method from steady-state gene expression data

Author(s) -

Hung-Cuong Trinh,

YungKeun Kwon

Publication year - 2021

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btab295

Subject(s) - inference , computer science , heuristic , gene regulatory network , boolean network , algorithm , expression (computer science) , data mining , theoretical computer science , boolean function , artificial intelligence , gene , gene expression , biology , genetics , programming language

It is a challenging problem in systems biology to infer both the network structure and dynamics of a gene regulatory network from steady-state gene expression data. Some methods based on Boolean or differential equation models have been proposed but they were not efficient in inference of large-scale networks. Therefore, it is necessary to develop a method to infer the network structure and dynamics accurately on large-scale networks using steady-state expression.

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