title: Reverse engineering of gene regulatory network using restricted gene expression programming.
abstract: Inference of gene regulatory networks has been becoming a major area of interest in the field of systems biology over the past decade. In this paper, we present a novel representation of S-system model, named restricted gene expression programming (RGEP), to infer gene regulatory network. A new hybrid evolutionary algorithm based on structure-based evolutionary algorithm and cuckoo search (CS) is proposed to optimize the architecture and corresponding parameters of model, respectively. Two synthetic benchmark datasets and one real biological dataset from SOS DNA repair network in E. coli are used to test the validity of our method. Experimental results demonstrate that our proposed method performs better than previously proposed popular methods.
authors.affiliation: 1 School of Information Science and Engineering, Zaozhuang University, Zaozhuang 277160, China.
authors.forename: Bin
authors.initials: B
authors.lastname: Yang
authors.affiliation: 1 School of Information Science and Engineering, Zaozhuang University, Zaozhuang 277160, China.
authors.forename: Sanrong
authors.initials: S
authors.lastname: Liu
authors.affiliation: 1 School of Information Science and Engineering, Zaozhuang University, Zaozhuang 277160, China.
authors.forename: Wei
authors.initials: W
authors.lastname: Zhang
mesh_terms: D000465:Algorithms; D004926:Escherichia coli / Q000235:genetics*; D020869:Gene Expression Profiling / Q000379:methods; D053263:Gene Regulatory Networks; D008957:Models, Genetic; D009154:Mutation; D013014:SOS Response, Genetics / Q000235:genetics*; D049490:Systems Biology
keywords: Gene regulatory network; cuckoo search; gene expression programming; hybrid evolutionary