Skip to main content
SpringerLink
Log in

Using Restricted Additive Tree Model for Identifying the Large-Scale Gene Regulatory Networks

  • Conference paper
  • First Online:
Intelligent Computing Theories and Methodologies (ICIC 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9226))

Included in the following conference series:

  • 1499 Accesses

Abstract

S-system model has been proposed to identify gene regulatory network in the past years. However, due to the computation complexity, this model is only used for reconstruction of small-scale networks. In this paper, the restricted additive tree model (RAT) is proposed to infer the large-scale gene regulatory networks. In the method, restricted additive tree model is used to encode the S-system model, and the hybrid evolution approach is used to optimize the structure and parameters of restricted additive tree. The large-scale gene regulatory network containing 30 genes is used to test the performance of our method. The results reveal that our method could identify the large-scale gene regulatory network correctly.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Srinath, S., Gunawan, R.: Parameter identifiability of power-law biochemical system models. J. Biotechnol. 149(3), 132–140 (2010)

    Article  Google Scholar 

  2. Wang, H., Qian, L., Dougherty, E.: Inference of gene regulatory networks using system: a unified approach. IET Syst. Biol. 4(2), 145–156 (2010)

    Article  Google Scholar 

  3. Akutsu, T., Miyano, S., Kuhara, S.: Inferring qualitative relations in genetic networks and metabolic pathways. Bioinformatics 16, 727–734 (2000)

    Article  Google Scholar 

  4. Liu, L.Z., Wu, F.X., Zhang, W.J.: Inference of biological S-system using the separable estimation method and the genetic algorithm. IEEE/ACM Trans. Comput. Biol. Bioinform. (TCBB). 9(4), 955–965 (2012)

    Article  MathSciNet  Google Scholar 

  5. Yeh, W.C., Lin, W.B., Hsieh, T.J., Liu, S.L.: Feasible prediction in S-system models of genetic networks. Expert Syst. Appl. 38(1), 193–197 (2011)

    Article  Google Scholar 

  6. Yeh, W.C., Hsieh, T.J.: Artificial bee colony algorithm-neural networks for S-system models of biochemical networks approximation. Neural Comput. Appl. 21(2), 365–375 (2012)

    Article  Google Scholar 

  7. Meskin, N., Nounou, H., Nounou, M., Datta, A., Dougherty, E.R.: Parameter estimation of Biological phenomena modeled by S-systems: an extended Kalman filter approach. In: 2011 50th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC), pp. 4424–4429. IEEE Press, New York (2011)

    Google Scholar 

  8. Tian, L.P., Mu, L., Wu, F.X.: Alternating constraint least squares parameter estimation for S-system models of biological networks. In: 4th International Conference on Bioinformatics and Biomedical Engineering (iCBBE), pp. 1–5 (2011)

    Google Scholar 

  9. Kikuchi, S., Tominaga, D., Arita, M., Takahashi, K.: Dynamic modeling of genetic networks using genetic algorithm and S-system. Bioinformatics 19, 643–650 (2003)

    Article  Google Scholar 

  10. Gonzalez, O.R., Kuper, C., Jung, K., Naval, P.C., Mendoza, E.: Parameter estimation using simulated annealing for S-system models of biochemical networks. Bioinformatics 23, 480–486 (2007)

    Article  Google Scholar 

  11. Palafox, L., Noman, N., Iba, H.: Reverse engineering of gene regulatory networks using dissipative particle swarm optimization evolutionary computation. IEEE Trans. 17(4), 577–587 (2013)

    Google Scholar 

  12. Yang, B., Jiang, M.Y., Chen, Y.H.: A fast and efficient method for inferring structure and parameters of S-system models. HIS 2011, 235–240 (2010)

    MATH  Google Scholar 

  13. Chellapilla, K.: Evolving computer programs without subtree crossover. IEEE Trans. Evol. Comput. 1, 209–216 (1997)

    Article  Google Scholar 

  14. Xie, F.X., Zhang, W.J., Yang, Z.L.: A dissipative particle swarm optimization. In: Proceedings of Congress on Evolutionary Computation (CEC), pp. 1456–1461 (2002)

    Google Scholar 

  15. Kimura, S., Ide, K., Kashihara, A.: Inference of S-system models of genetic networks using a cooperative coevolutionary algorithm. Bioinformatics 21, 1154–1163 (2005)

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported the PhD research startup foundation of Zaozhuang University (No. 1020702).

Author information

Authors and Affiliations

  1. School of Information Science and Engineering, Zaozhuang University, Zaozhuang, China

    Bin Yang & Wei Zhang

Authors
  1. Bin Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wei Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bin Yang .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

  3. Liverpool John Moores University, Liverpool, United Kingdom

    Abir Hussain

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Yang, B., Zhang, W. (2015). Using Restricted Additive Tree Model for Identifying the Large-Scale Gene Regulatory Networks. In: Huang, DS., Jo, KH., Hussain, A. (eds) Intelligent Computing Theories and Methodologies. ICIC 2015. Lecture Notes in Computer Science(), vol 9226. Springer, Cham. https://doi.org/10.1007/978-3-319-22186-1_34

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-22186-1_34

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-22185-4

  • Online ISBN: 978-3-319-22186-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation