Advertisement

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)

Abstract

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.

Keywords

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

Notes

Acknowledgements

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

References

  1. 1.
    Ellwanger, D.C., Leonhardt, J.F., Mewes, H.W.: Large-scale modeling of condition-specific gene regulatory networks by information integration and inference. Nucleic Acids Res. 42(21), e166 (2014)CrossRefGoogle Scholar
  2. 2.
    Vera-Licona, P., Jarrah, A., Garcia-Puente, L.D., McGee, J., Laubenbacher, R.: An algebra-based method for inferring gene regulatory networks. BMC Syst. Biol. 8, 37 (2014)CrossRefGoogle Scholar
  3. 3.
    Xie, Y., Wang, R., Zhu, J.: Construction of breast cancer gene regulatory networks and drug target optimization. Arch. Gynecol. Obstet. 290(4), 749–755 (2014)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Penfold, C.A., Millar, J.B., Wild, D.L.: Inferring orthologous gene regulatory networks using interspecies data fusion. Bioinformatics 31(12), i97–i105 (2015)CrossRefGoogle Scholar
  5. 5.
    Baur, B., Bozdag, S.: A canonical correlation analysis-based dynamic bayesian network prior to infer gene regulatory networks from multiple types of biological data. J. Comput. Biol. 22(4), 289–299 (2015)CrossRefGoogle Scholar
  6. 6.
    Yang, M., Li, R., Chu, T.: Construction of a Boolean model of gene and protein regulatory network with memory. Neural Netw. 52, 18–24 (2014)CrossRefzbMATHGoogle Scholar
  7. 7.
    Adabor, E.S., Acquaah-Mensah, G.K., Oduro, F.T.: SAGA: a hybrid search algorithm for Bayesian Network structure learning of transcriptional regulatory networks. J. Biomed. Inform. 53, 27–35 (2015)CrossRefGoogle Scholar
  8. 8.
    Sun, M., Cheng, X., Socolar, J.E.: Causal structure of oscillations in gene regulatory networks: Boolean analysis of ordinary differential equation attractors. Chaos 23(2), 025104 (2013)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Wang, J., Chen, B., Wang, Y., Wang, N., Garbey, M., Tran-Son-Tay, R., Berceli, S.A., Wu, R.: Reconstructing regulatory networks from the dynamic plasticity of gene expression by mutual information. Nucleic Acids Res. 41(8), e97 (2013)CrossRefGoogle Scholar
  10. 10.
    Maetschke, S.R., Madhamshettiwar, P.B., Davis, M.J., Ragan, M.A.: Supervised, semi-supervised and unsupervised inference of gene regulatory networks. Brief Bioinform. 15(2), 195–211 (2014)CrossRefGoogle Scholar
  11. 11.
    Cerulo, L., Elkan, C., Ceccarelli, M.: Learning gene regulatory networks from only positive and unlabeled data. BMC Bioinform. 11, 228 (2010)CrossRefGoogle Scholar
  12. 12.
    Mordelet, F., Vert, J.P.: SIRENE: supervised inference of regulatory networks. Bioinformatics 24(16), i76–i82 (2008)CrossRefGoogle Scholar
  13. 13.
    Gillani, Z., Akash, M.S., Rahaman, M.D., Chen, M.: CompareSVM: supervised, Support Vector Machine (SVM) inference of gene regularity networks. BMC Bioinform. 15, 395 (2014)CrossRefGoogle Scholar
  14. 14.
    Taher, N., Ehsan, A., Jabbari, M.: A new hybrid evolutionary algorithm based on new fuzzy adaptive PSO and NM algorithms for distribution feeder reconfiguration. Energy Convers. Manage. 54(1), 7–16 (2011)Google Scholar
  15. 15.
    Kuan-Cheng, L., Yi-Hsiu, H.: Classification of medical datasets using SVMs with hybrid evolutionary algorithms based on endocrine-based particle swarm optimization and artificial bee colony algorithms. J. Med. Syst. 39, 119 (2015)CrossRefGoogle Scholar
  16. 16.
    Reza, M., Fatemi, G., Farzad, Z.: A new hybrid evolutionary based RBF networks method for forecasting time series: a case study of forecasting emergency supply demand time series. Eng. Appl. Artif. Intell. 36, 204–214 (2014)CrossRefGoogle Scholar
  17. 17.
    Jia, W., Zhao, D., Shen, T., Su, C., Hu, C., Zhao, Y.: A new optimized GA-RBF neural network algorithm. Comput. Intell. Neurosci. 2014, 6 (2014). 982045Google Scholar
  18. 18.
    Xie, F.X., Zhang, W.J., Yang, Z.L.: A dissipative particle swarm optimization. In: Congress on Evolutionary Computation (CEC), pp. 1456–1461 (2002)Google Scholar
  19. 19.
    Yang, X.-S.: Firefly algorithms for multimodal optimization. In: Watanabe, O., Zeugmann, T. (eds.) SAGA 2009. LNCS, vol. 5792, pp. 169–178. Springer, Heidelberg (2009). doi: 10.1007/978-3-642-04944-6_14 CrossRefGoogle Scholar
  20. 20.
    Butte, A.J., Tamayo, P., Slonim, D., Golub, T.R., Kohane, I.S.: Discovering functional relationships between RNA expression and chemotherapeutic susceptibility using relevance networks. Proc. Natl. Acad. Sci. U.S.A. 97(22), 12182–12186 (2000)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.School of Information Science and EngineeringZaozhuang UniversityZaozhuangChina

Personalised recommendations