A Boolean network inference from time-series gene expression data using a genetic algorithm | Zendy

Shohag Barman | Zendy; YungKeun Kwon | Zendy

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A Boolean network inference from time-series gene expression data using a genetic algorithm

Author(s) -

Shohag Barman,

YungKeun Kwon

Publication year - 2018

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/bty584

Subject(s) - inference , gene regulatory network , computer science , boolean network , scalability , algorithm , genetic algorithm , boolean function , data mining , computational biology , gene , biology , artificial intelligence , machine learning , gene expression , genetics , database

Inferring a gene regulatory network from time-series gene expression data is a fundamental problem in systems biology, and many methods have been proposed. However, most of them were not efficient in inferring regulatory relations involved by a large number of genes because they limited the number of regulatory genes or computed an approximated reliability of multivariate relations. Therefore, an improved method is needed to efficiently search more generalized and scalable regulatory relations.

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