title: Learning restricted Boolean network model by time-series data.
abstract: Restricted Boolean networks are simplified Boolean networks that are required for either negative or positive regulations between genes. Higa et al. (BMC Proc 5:S5, 2011) proposed a three-rule algorithm to infer a restricted Boolean network from time-series data. However, the algorithm suffers from a major drawback, namely, it is very sensitive to noise. In this paper, we systematically analyze the regulatory relationships between genes based on the state switch of the target gene and propose an algorithm with which restricted Boolean networks may be inferred from time-series data. We compare the proposed algorithm with the three-rule algorithm and the best-fit algorithm based on both synthetic networks and a well-studied budding yeast cell cycle network. The performance of the algorithms is evaluated by three distance metrics: the normalized-edge Hamming distance [Formula: see text], the normalized Hamming distance of state transition [Formula: see text], and the steady-state distribution distance μ (ssd). Results show that the proposed algorithm outperforms the others according to both [Formula: see text] and [Formula: see text], whereas its performance according to μ (ssd) is intermediate between best-fit and the three-rule algorithms. Thus, our new algorithm is more appropriate for inferring interactions between genes from time-series data.
authors.affiliation: Department of Physics and Electronic Information Engineering, Wenzhou University, Wenzhou 325035, Zhejiang, China.
authors.forename: Hongjia
authors.initials: H
authors.lastname: Ouyang
authors.affiliation: Department of Physics and Electronic Information Engineering, Wenzhou University, Wenzhou 325035, Zhejiang, China.
authors.forename: Jie
authors.initials: J
authors.lastname: Fang
authors.affiliation: Department of Physics and Electronic Information Engineering, Wenzhou University, Wenzhou 325035, Zhejiang, China.
authors.forename: Liangzhong
authors.initials: L
authors.lastname: Shen
authors.affiliation: Department of Electrical and Computer Engineering, Texas A&M University, College Station 33101, TX, USA ; Computational Biology Division, Translational Genomics Research Institute, Phoenix 77843, AZ, USA.
authors.forename: Edward R
authors.initials: ER
authors.lastname: Dougherty
authors.affiliation: Department of Physics and Electronic Information Engineering, Wenzhou University, Wenzhou 325035, Zhejiang, China ; Department of Electrical and Computer Engineering, Texas A&M University, College Station 33101, TX, USA.
authors.forename: Wenbin
authors.initials: W
authors.lastname: Liu
keywords: Budding yeast cell cycle; Inference; Restricted Boolean network
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