Inference of Gene Regulatory Network Based on Radial Basis Function Neural Network

  • Sanrong Liu
  • Bin YangEmail author
  • Haifeng Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10122)


Inference of gene regulatory network (GRN) from gene expression data is still a challenging work. The supervised approaches perform better than unsupervised approaches. In this paper, we develop a new supervised approach based on radial basis function (RBF) neural network for inference of gene regulatory network. A new hybrid evolutionary method based on dissipative particle swarm optimization (DPSO) and firefly algorithm (FA) is proposed to optimize the parameters of RBF. The data from E.coli network is used to test our method and results reveal that our method performs than classical approaches.


Gene regulatory network Radial basis function neural network Particle swarm optimization Firefly algorithm 



This work was supported by the PhD research startup foundation of Zaozhuang University (No. 2014BS13), and Shandong Provincial Natural Science Foundation, China (No. ZR2015PF007).


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© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.School of Information Science and EngineeringZaozhuang UniversityZaozhuangChina

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