Skip to main content
SpringerLink
Log in
Book cover

Advanced Computing and Systems for Security pp 87–99Cite as

Inference of Gene Regulatory Networks with Neural-Cuckoo Hybrid

  • Chapter
  • First Online:

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 395))

Abstract

Current progress in cellular biology and bioinformatics allow researchers to get a distinct picture of the complex biochemical processes those occur within a cell of the human body and remain as the cause for many diseases. Therefore, this technology opened up a new door to the researchers of computer science as well as to biologists to work together to investigate the causes of a disease. One of the greatest challenges of the post-genomic era is the investigation and inference of the regulatory interactions or dependencies between genes from the microarray data. Here, a new methodology has been devised for investigating the genetic interactions among genes from temporal gene expression data by combining the features of Neural Network and Cuckoo Search optimization. The developed technique has been applied on the real-world microarray dataset of Lung Adenocarcinoma for detection of genes which may be directly responsible for the cause of Lung Adenocarcinoma.

This is a preview of subscription content, 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

Learn about institutional subscriptions

References

  1. National Center for Biotechnology Information (NCBI).: Microarrays: Chipping Away at the Mysteries of Science and Medicine, vol. 2004 NCBI, Bethesda (2004)

    Google Scholar 

  2. Masys, D.R.: Linking microarray data to the literature. Nat. Genet. 28, 9–10 (2001)

    Google Scholar 

  3. Akutsu, T., Miyano, S., Kuhara, S.: Algorithms for inferring qualitative models of biological networks. In: Proceeding of Pacific Symposium on Biocomputing, 5, pp. 293–304 (2000)

    Google Scholar 

  4. Vijesh, N., Chakrabarty, S.K., Sreekumar, J.: Modelling of gene regulatory network: a review. J. Biomed. Sci. Eng. 6, 223–231 (2013)

    Article  Google Scholar 

  5. Liang, S., Fuhrman, S., Somogyi, R.: REVEAL, a general reverse engineering algorithm for inference of genetic network architectures. In: Proceeding of Pacific Symposium on Biocomputing, vol. 3, pp. 18–29 (1998)

    Google Scholar 

  6. Akutsu, T., Miyano, S., Kuhara, S.: Identification of genetic networks from a small number of gene expression patterns under the boolean network model. In: Proceeding of Pacific Symposium on Biocomputing, vol. 4, pp. 17–28 (1999)

    Google Scholar 

  7. Weaver, D.C., Workman, C.T., Stormo, G.D.: Modeling regulatory networks with weight matrices. In: Proceeding of Pacific Symposium on Biocomputing, vol. 4, pp. 112–123 (1999)

    Google Scholar 

  8. Drugan, M.M., Wiering, M.A.: Feature selection for Bayesian network classifiers using the MDL-FS score. Int. J. Approximate Reasoning 51(6), 695–717 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  9. Bielza, C., Larrañaga, P.: Discrete Bayesian network classifiers: a survey.  ACM Comput. Surv. 47(1, article 5), 1–43 (2014)

    Google Scholar 

  10. Murphy, K., Mian, S.: Modelling gene expression data using dynamic bayesian networks. In: Computer Science Division. University of California, Berkeley (1999)

    Google Scholar 

  11. Murphy, K.P.: Dynamic bayesian networks: representation, inference, and learning. In: Computer Science, p. 255. University of California, Berkeley (2002)

    Google Scholar 

  12. Perrin, B.E., Ralaivola, L., Mazurie, A., Bottani, S., Mallet, J., D’Alche-Buc, F.: Gene networks inference using dynamic Bayesian networks. Bioinformatics 19(suppl. 2), II138–II148 (2003)

    Google Scholar 

  13. Wang, H., Quin, L., Dougherty, E.: Inference of gene regulatory network using S-system: a unified approach. In: Proceeding of 2007 IEEE Symposium CIBCB, pp. 82–89 (2007)

    Google Scholar 

  14. Nakayama, T., Seno, S., Takenaka, Y., Matsuda, H.: Inference of gene regulatory networks using immune algorithm. J. Bioinform. Comput. Biol. 9, 75–86 (2011)

    Article  Google Scholar 

  15. Du, P.P., Gong, J., Wurtele, E.S., Dickerson, J.A.: Modeling gene expression networks using fuzzy logic. IEEE Trans. Syst. Man Cybern. 35, 1351–1359 (2005)

    Article  Google Scholar 

  16. Dickerson, J.A., Cox, Z., Wurtele, E.S., Fulmer, A.W.: Creating metabolic and regulatory network models using fuzzy cognitive maps. In: Proceeding of North American Fuzzy Information Processing Conference (NAFIPS). Vancouver, B.C. (2001)

    Google Scholar 

  17. Vohradsky, J.: Neural network model of gene expression. FASEB J. 15, 846–854 (2001)

    Article  Google Scholar 

  18. Xu, R., Wunsch II, D., Frank, R.: Inference of genetic regulatory networks with recurrent neural network models using particle swarm optimization. IEEE/ACM Trans. Comput. Biol. Bioinform. 4(4), 681–692 (2007)

    Article  Google Scholar 

  19. Noman, N., Palafox, L., Iba, H.: Reconstruction of gene regulatory networks from gene expression data using decoupled recurrent neural network model. In: Natural Computing and Beyond (Springer), PICT6, pp. 93–103 (2013)

    Google Scholar 

  20. Ioannis, A.M., Andrei, D., Dimitris, T.: Gene regulatory networks modeling using a dynamic evolutionary hybrid. BMC Bioinform. 11, 1–17 (2010)

    Google Scholar 

  21. Keedwell, E., Narayanan, A.: Discovering gene networks with a neural-genetic hybrid. IEEE/ACM Trans. Comput. Biol. Bioinform. 2(3), 231–242 (2005)

    Article  Google Scholar 

  22. Kentzoglanakis, K., Poole, M.: A swarm intelligence framework for reconstructing gene networks: searching for biologically plausible architecture. IEEE/ACM Trans. Comput. Biol. Bioinform. 9(2), 355–371 (2012)

    Article  Google Scholar 

  23. Xu, R., Wunsch II, D.C., Frank, R.L.: Inference of genetic regulatory networks with recurrent neural network models using particle swarm optimization. IEEE/ACM Trans. Comput. Biol. Bioinform. 4(4), 681–692 (2007)

    Article  Google Scholar 

  24. Rakshit, P., Das, P., Konar, A., Nasipuri, M., Janarthan R.: A recurrent fuzzy neural network model of a gene regulatory for knowledge extraction using invasive weed and artificial bee colony optimization algorithm. In: Proceeding of 1st International Conference on Recent Advances in Information Technology (RAIT) (2012)

    Google Scholar 

  25. Mandal, S., Saha, G., Pal, R.K.: S-system based gene regulatory network reconstruction using Firefly algorithm. In: Proceeding of Third International Conference on Computer, Communication, Control and Information Technology (C3IT), pp. 1–5 (2015)

    Google Scholar 

  26. Yang, X.S.: Nature-Inspired Metaheuristic algorithms, pp. 105–116, 2nd edn. Luniver Press, London (2010)

    Google Scholar 

  27. Jereesh, A.S., Govindan, V.K.: Gene regulatory network modeling using cuckoo search and S-system. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 3(9), 1231–1237 (2013)

    Google Scholar 

  28. Yang, X.S., Deb, S.: Cuckoo search via Lévy flights. In: Proceedings of World Congress on Nature & Biologically Inspired Computing (NaBIC 2009), pp. 210–214 (2009)

    Google Scholar 

  29. National Center for Biotechnology Information. http://www.ncbi.nlm.nih.gov

  30. Mandal, S., Saha, G., Pal, R.K.: Reconstruction of dominant gene regulatory network from microarray data using rough set and bayesian approach. J. Comput. Sci. Syst. Biol. 6(5), 262–270 (2013)

    Article  Google Scholar 

  31. Mandal, S., Saha, G.: Rough set theory based automated disease diagnosis using lung adenocarcinoma as a test case. SIJ Trans. Comput. Sci. Eng. Appl. (CSEA) 1(3), 59–66 (2013)

    Google Scholar 

  32. Database for Annotation, Visualization and Integrated Discovery. http://david.abcc.ncifcrf.gov

  33. Gene Cards. http://www.genecards.org

  34. Civicioglu, P., Besdok, E.: A conceptual comparison of the cuckoo-search, particle swarm optimization, differential evolution and artificial bee colony algorithms. Artif. Intell. Rev. 1–32 (2013)

    Google Scholar 

  35. Mandal, S., Saha, G., Pal, R.K.: Neural network based gene regulatory network reconstruction. In: Proceedings of Third International Conference on Computer, Communication, Control and Information Technology (C3IT), pp. 1–5 (2015)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. ECE Department, GIMT, Krishna Nagar, India

    Sudip Mandal

  2. IT Department, NEHU, Shillong, India

    Goutam Saha

  3. CSE Department, University of Calcutta, Kolkata, India

    Rajat K. Pal

Authors
  1. Sudip Mandal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Goutam Saha
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rajat K. Pal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sudip Mandal .

Editor information

Editors and Affiliations

  1. A.K. Choudhury School of Information Technology, University of Calcutta, Kolkata, West Bengal, India

    Rituparna Chaki

  2. Università Ca' Foscari, Venice, Italy

    Agostino Cortesi

  3. Bialystok University of Technology Faculty of Computer Science, Bialystok, Poland

    Khalid Saeed

  4. University of Calcutta, Kolkata, West Bengal, India

    Nabendu Chaki

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer India

About this chapter

Cite this chapter

Mandal, S., Saha, G., Pal, R.K. (2016). Inference of Gene Regulatory Networks with Neural-Cuckoo Hybrid. In: Chaki, R., Cortesi, A., Saeed, K., Chaki, N. (eds) Advanced Computing and Systems for Security. Advances in Intelligent Systems and Computing, vol 395. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2650-5_6

Download citation

  • DOI: https://doi.org/10.1007/978-81-322-2650-5_6

  • Published:

  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-2648-2

  • Online ISBN: 978-81-322-2650-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation