Skip to main content
SpringerLink
Log in

Gene Regulatory Network Construction Parallel Technique Based on Network Component Analysis

  • Conference paper
  • First Online:
Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020) (AICV 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1153))

Abstract

Construction of a gene regulatory network is a vital process for understanding the gene functions and gene influences on the other genes. Furthermore, gene regulatory network analysis is a promising method for demonstrating the topological order of genes interactions. One technique for constructing a gene regulatory network is FastNCA that is based on network component analysis methodology. Although FastNCA is widely used to construct the gene regulatory network of cancer diseases associated genes, it is a time-consuming and computational intensive technique. As a result, this paper presents an enhanced parallel implementation of FastNCA that uses distributed parallelism methodology to enhance the performance of FastNCA. Different gene datasets are used to evaluate the performance of the proposed algorithm. The experimental results demonstrate that the proposed algorithm outperforms FastNCA. Where the achieved speedup is up to 250 on 256 processing nodes.

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 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
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. Aluru, S.: Handbook of Computational Molecular Biology. CRC Press, Boca Raton (2006)

    MATH  Google Scholar 

  2. Bolouri, H.: Computational Modeling of Gene Regulatory Networks a Primer. World Scientific Publishing Company, Singapore (2008)

    Book  Google Scholar 

  3. Pirgazi, J., Khanteymoori, A.R.: A robust gene regulatory network inference method base on Kalman filter and linear regression. PLoS One 13(7), e0200094 (2018)

    Article  Google Scholar 

  4. Lam, K.Y., Westrick, Z.M., Muller, C.L., Christiaen, L., Bonneau, R.: Fused regression for multi-source gene regulatory network inference. PLoS Comput. Biol. 12(12), e1005157 (2016)

    Article  Google Scholar 

  5. Omranian, N., Eloundou-Mbebi, J.M.O., Mueller-Roeber, B., Nikoloski, Z.: Gene regulatory network inference using fused LASSO on multiple data sets. Sci. Rep. 6, 20533 (2016)

    Article  Google Scholar 

  6. Guerrier, S., Mili, N., Molinari, R., Orso, S., Avella-Medina, M., Ma, Y.: A predictive based regression algorithm for gene network selection. Front. Genet. 7, 97 (2016)

    Article  Google Scholar 

  7. Gregoretti, F., Belcastro, V., Di Bernardo, D., Oliva, G.: A parallel implementation of the network identification by multiple regression (NIR) algorithm to reverse-engineer regulatory gene networks. PLoS One 5(4), e10179–e10183 (2010)

    Article  Google Scholar 

  8. Jostins, L., Jaeger, J.: Reverse engineering a gene network using an asynchronous parallel evolution strategy. BMC Syst. Biol. 4(1), 17–33 (2010)

    Article  Google Scholar 

  9. Klinger, B., Bluthgen, N.: Reverse engineering gene regulatory networks by modular response analysis-a benchmark. Essays Biochem. 62(4), 535–547 (2018)

    Article  Google Scholar 

  10. Perkins, M., Daniels, K.: Visualizing dynamic gene interactions to reverse engineer gene regulatory networks using topological data analysis. In: 2017 21st International Conference Information Visualisation (IV) (2017)

    Google Scholar 

  11. Liu, Z.-P.: Reverse engineering of genome-wide gene regulatory networks from gene expression data. Curr. Genomics 16(1), 3–22 (2015)

    Article  Google Scholar 

  12. de Souza, M.C., Higa, C.H.A.: Reverse engineering of gene regulatory networks combining dynamic Bayesian networks and prior biological knowledge. In: International Conference on Computational Science and Its Applications (2018)

    Google Scholar 

  13. Villaverde, A.F., Banga, J.R.: Reverse engineering and identification in systems biology: strategies, perspectives and challenges. J. Roy. Soc. Interface 11(91), 1–16 (2014)

    Article  Google Scholar 

  14. Sales, G., Romualdi, C.: parmigene—A parallel R package for mutual information estimation and gene network reconstruction. Bioinformatics 27(13), 1876–1877 (2011)

    Article  Google Scholar 

  15. Shi, H., Schmidt, B., Liu, W., Muller-Wittig, W.: Parallel mutual information estimation for inferring gene regulatory networks on GPUs. BMC Res. Notes 4(1), 189–194 (2011)

    Article  Google Scholar 

  16. Zhang, X., Zhao, X.-M., He, K., Lu, L., Cao, Y., Liu, J., Hao, J.-K., Liu, Z.-P., Chen, L.: Inferring gene regulatory networks from gene expression data by path consistency algorithm based on conditional mutual information. Bioinformatics 28(1), 98–104 (2011)

    Article  Google Scholar 

  17. Meyer, P.E., Lafitte, F., Bontempi, G.: minet: AR/Bioconductor package for inferring large transcriptional networks using mutual information. BMC Bioinf. 9(1), 461 (2008)

    Article  Google Scholar 

  18. Lachmann, A., Giorgi, F.M., Lopez, G., Califano, A.: ARACNe-AP: gene network reverse engineering through adaptive partitioning inference of mutual information. Bioinformatics 32(14), 2233–2235 (2016)

    Article  Google Scholar 

  19. Barman, S., Kwon, Y.-K.: A novel mutual information-based Boolean network inference method from time-series gene expression data. PLoS One 12(2), e0171097 (2017)

    Article  Google Scholar 

  20. Yan, X., Mehan, M.R., Huang, Y., Waterman, M.S., Yu, P.S., Zhou, X.J.: A graph-based approach to systematically reconstruct human transcriptional regulatory modules. Bioinformatics 23(13), i577–i586 (2007)

    Article  Google Scholar 

  21. Jiang, H., Turki, T., Zhang, S., Wang, J.T.L.: Reverse engineering gene regulatory networks using graph mining. In: International Conference on Machine Learning and Data Mining in Pattern Recognition (2018)

    Google Scholar 

  22. Raychaudhuri, S., Stuart, J.M., Altman, R.B.: Principal components analysis to summarize microarray experiments: application to sporulation time series. In: Pacific Symposium on Biocomputing, pp. 455–466. NIH Public Access (2000)

    Google Scholar 

  23. Holter, N.S., Mitra, M., Maritan, A., Cieplak, M., Banavar, J.R., Fedoroff, N.V.: Fundamental patterns underlying gene expression profiles: simplicity from complexity. Proc. Nat. Acad. Sci. 97(15), 8409–8414 (2000)

    Article  Google Scholar 

  24. Hyvarinen, A., Karhunen, J., Oja, E.: Independent Component Analysis. Wiley, Hoboken (2001)

    Book  Google Scholar 

  25. Aapo, H.: Fast and robust fixed-point algorithms for independent component analysis. IEEE Trans. Neural Netw. 10(3), 626–634 (1999)

    Article  Google Scholar 

  26. Liebermeister, W.: Linear modes of gene expression determined by independent component analysis. Bioinformatics 18(1), 51–60 (2002)

    Article  Google Scholar 

  27. Liao, J.C., Boscolo, R., Yang, Y.-L., Tran, L.M., Sabatti, C., Roychowdhury, V.P.: Network component analysis: reconstruction of regulatory signals in biological systems. In: Proceedings of the National Academy of Sciences (2003)

    Google Scholar 

  28. Chang, C., Ding, Z., Hung, Y.S., Fung, P.C.W.: Fast network component analysis (FastNCA) for gene regulatory network reconstruction from microarray data. Bioinformatics 24(11), 1349–1358 (2008)

    Article  Google Scholar 

  29. Jayavelu, N.D., Aasgaard, L.S., Bar, N.: Iterative sub-network component analysis enables reconstruction of large scale genetic networks. BMC Bioinf. 16(1), 366 (2015)

    Article  Google Scholar 

  30. Elsayad, D., Ali, A., Shedeed, H.A., Tolba, M.F.: PFastNCA: parallel fast network component analysis for gene regulatory network. In: International Conference on Advanced Machine Learning Technologies and Applications (2018)

    Google Scholar 

  31. Shi, Q., Zhang, C., Guo, W., Zeng, T., Lu, L., Jiang, Z., Wang, Z., Liu, J., Chen, L.: Local network component analysis for quantifying transcription factor activities. Methods 124, 25–35 (2017)

    Article  Google Scholar 

  32. Noor, A., Ahmad, A., Serpedin, E., Nounou, M., Nounou, H.: ROBNCA: robust network component analysis for recovering transcription factor activities. Bioinformatics 29(19), 2410 (2013)

    Article  Google Scholar 

  33. Noor, A., Ahmad, A., Serpedin, E.: SparseNCA: sparse network component analysis for recovering transcription factor activities with incomplete prior information. IEEE/ACM Trans. Comput. Biol. Bioinf. 15(2), 387–395 (2018)

    Article  Google Scholar 

  34. Wei, K., Qianni, S., Shuaiqun, W.: Transcriptional regulation analysis of breast cancer based on FastNCA algorithm. Genomics Appl. Biol. 8, 78 (2018)

    Google Scholar 

  35. Sun, Q., Kong, W., Mou, X., Wang, S.: Transcriptional regulation analysis of Alzheimer’s disease based on FastNCA algorithm. Curr. Bioinf. 14(8), 771–782 (2019)

    Article  Google Scholar 

  36. Kumar, V.: Introduction to Parallel Computing. Addison-Wesley Longman Publishing Co., Inc., Boston (2002)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt

    Dina Elsayad, Safwat Hamad, Howida A. Shedeed & Mohamed F. Tolba

Authors
  1. Dina Elsayad
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Safwat Hamad
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Howida A. Shedeed
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mohamed F. Tolba
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dina Elsayad .

Editor information

Editors and Affiliations

  1. Information Technology Department, Cairo University, Faculty of Computers and Information, Giza, Egypt

    Aboul-Ella Hassanien

  2. Faculty of Computers and Information, Benha University, Banha, Egypt

    Ahmad Taher Azar

  3. Faculty of Computers and Information, Suez Canal University, Ismailia, Egypt

    Tarek Gaber

  4. Departamento de Ciencias Computacionales, Universidad de Guadalajara, CUCEI, Guadajalara, Jalisco, Mexico

    Diego Oliva

  5. Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt

    Fahmy M. Tolba

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Elsayad, D., Hamad, S., Shedeed, H.A., Tolba, M.F. (2020). Gene Regulatory Network Construction Parallel Technique Based on Network Component Analysis. In: Hassanien, AE., Azar, A., Gaber, T., Oliva, D., Tolba, F. (eds) Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020). AICV 2020. Advances in Intelligent Systems and Computing, vol 1153. Springer, Cham. https://doi.org/10.1007/978-3-030-44289-7_80

Download citation

Publish with us

Policies and ethics

Search

Navigation