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author:

Li, Xiao-Min (Li, Xiao-Min.) [1] | Peng, Zheng (Peng, Zheng.) [2] | Zhu, Wenxing (Zhu, Wenxing.) [3] (Scholars:朱文兴)

Indexed by:

CPCI-S

Abstract:

Probabilistic Boolean network (PBN) is widely used in modeling genetic regulatory networks, which main task is to construct a sparse probabilistic Boolean networks (PBNs) based on a given transition-probability matrix and a set of Boolean networks (BNs). In this paper, a new alternating direction method of multipliers is proposed for achieving this purpose. At each iteration of the proposed method, three subproblems need to be solved and a multiplier updating with closed form needs to be performed. The former two subproblems are solved in a parallel fashion, while the last subproblem is handled in an alternative fashion with the former two. The proposed method can be interpreted as a classical alternating direction method of multipliers with an operator splitting. All subproblem solvers do not involve matrix computation, and consequently, the proposed method can be directly used to solve very large scale problem. Some numerical experiments demonstrate that efficiency and validity of the proposed method with comparison to some existing methods.

Keyword:

Alternating direction method of multipliers Genetic regulatory networks L-1/2-regularization Separable minimization Sparse probabilistic Boolean networks

Community:

  • [ 1 ] [Li, Xiao-Min]Shijiazhang Inst Technol, Dept Publ Educ, Shijiazhuang 050228, Peoples R China
  • [ 2 ] [Peng, Zheng]Fuzhou Univ, Coll Math & Comp Sci, Fuzhou 350108, Peoples R China
  • [ 3 ] [Zhu, Wenxing]Fuzhou Univ, Coll Math & Comp Sci, Fuzhou 350108, Peoples R China

Reprint 's Address:

  • [Li, Xiao-Min]Shijiazhang Inst Technol, Dept Publ Educ, Shijiazhuang 050228, Peoples R China

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Source :

2014 10TH INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION (ICNC)

ISSN: 2469-8814

Year: 2014

Page: 790-796

Language: English

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 0

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