Advertisement

Impact of Memory Space Optimization Technique on Fast Network Motif Search Algorithm

  • HimanshuEmail author
  • Sarika Jain
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 553)

Abstract

In this paper we propose PATCOMP—a PARTICIA-based novel approach for Network motif search. The algorithm takes advantage of compression and speed of PATRICIA data structure to store the collection of subgraphs in memory and search for classification and census of network. Paper also describes the structure of PATRICIA nodes and how data structure is developed for using it for counting of subgraphs. The main benefit of this approach is significant reduction in memory space requirement particularly for larger network motifs with acceptable time performance. To assess the effectiveness of PATRICIA-based approach we compared the performance (memory and time) of this proposed approach with QuateXelero. The experiments with different networks like ecoli and yeast validate the advantage of PATRICIA-based approach in terms of reduction in memory usage by 4.4–20% for E. coli and 5.8–23.2% for yeast networks.

Keywords

Bioinformatics Network motifs Biological networks Complex networks Algorithms Data structures PATRICIA Optimization 

Notes

Acknowledgements

Authors express their deep sense of gratitude to the Founder President of Amity University, Dr. Ashok K. Chauhan, for his keen interest in promoting research in the Amity University and has always been an inspiration for achieving greater heights.

References

  1. 1.
    R, Milo, S Shen-Orr, S Itzkovitz, N Kashtan, D Chklovskii, and U Alon. “Network motifs: Simple building blocks of complex networks.” Science 298 (2002): 824–827.Google Scholar
  2. 2.
    Wong, E, B Baur, S Quader, et al. “Biological network motif detection: principles and practice.” BriefBioinform 13, no. 2 (2001): 202–15.Google Scholar
  3. 3.
    Wernicke, S, and F Rasche. “FANMOD: a tool for fast network motif detection.” Bioinformatics 22 (2006): 1152–1153.Google Scholar
  4. 4.
    Kashani, Z R, H Ahrabian, E Elahi, A Nowzari-Dalini, and et al. “Kavosh: a new algorithm for finding network motifs.” BMC bioinformatics 10 (2009): 318.Google Scholar
  5. 5.
    Ribeiro, P, and F Silva. “Efficient subgraph frequency estimation with g-tries.” International workshop on algorithms in bioinformatics (WABI), LNCS. Springer, 2010. 238–249.Google Scholar
  6. 6.
    Khakabimamaghani, S, I Sharafuddin, N Dichter, et al. “QuateXelero: an accelerated exact network motif detection algorithm.” PLoSOne 8, no. 7 (2013).Google Scholar
  7. 7.
    McKay, B D. “Practical graph isomorphism.” 10th Manitoba conference on numerical mathematics and computing. Congressus Numerantium, 1981. 45–87.Google Scholar
  8. 8.
    Tran, NgocTam L, Sominder Mohan, Zhuoqing Xu, and Chun-Hsi Huang. “Current innovations and future challenges of network motif detection.” Briefings in Bioinformatics, 2014: 1–29.Google Scholar
  9. 9.
    Robert, Sedgewick. Algorithms. Addison Wesley, 1984.Google Scholar
  10. 10.
    Warnicke, S. “Efficient detection of network motifs.” IEEE/ACM Transactions on Computational Biology and Bioinformatics 3, no. 4 (2006): 347–359.Google Scholar
  11. 11.
    The E.coli Database. Available: http://www.kegg.com/.Google Scholar
  12. 12.
    The S. cerevisiae Database. Available: http://www.weizmann.ac.il/mcb/UriAlon/.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  1. 1.Amity Institute of Information TechnologyAmity UniversityNoidaIndia

Personalised recommendations