吉林大学学报(工学版) ›› 2017, Vol. 47 ›› Issue (2): 624-631.doi: 10.13229/j.cnki.jdxbgxb201702038

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Reconstruction for gene regulatory network based on hybrid parallel genetic algorithm and threshold value method

ZHENG Ming1, 2, 3, ZHUO Mu-gui2, ZHANG Shu-gong1, ZHOU You3, LIU Gui-xia3   

  1. 1.College of Mathematics, Jilin University, Changchun 130012, China;
    2.College of Information and Electronic Engineering, Wuzhou University, Wuzhou 543002, China;
    3.College of Computer Science and Technology,Jilin University, Changchun 130012, China
  • Received:2015-11-14 Online:2017-03-20 Published:2017-03-20

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Abstract: In order to improve the efficiency of the Gene Regulatory Networks (GRNs) reconstruction, a novel algorithm based on hybrid parallel genetic algorithm and threshold value method was proposed. Two parts were included in this algorithm, the solution decomposition and parameter calculation. In solution decomposition, Singular Value Decomposition (SVD) method was used to obtain the solutions, which are capable in math framework. The threshold value method was used to reduce the unnecessary edges in the network, which can improve the efficiency and is suitable for the rules in bioinformatics. In parameter calculation, parallel genetic algorithm was used for optimizing the parameters in the whole solution space and hill climbing method was used to calculate the solutions in small region. In experiments, the proposed algorithm was validated on both melanoma data and diabetes data. The results of this work were compared with the results of real network in bioinformatics and the results of genetic algorithm and swarm particle algorithm, which demonstrate the higher efficiency of the proposed method.

Key words: artificial intelligence, hybrid parallel genetic algorithm, threshold value method, singular value decomposition, differential equation model, gene regulatory network

CLC Number: 

  • TP18
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