Skip to main content
SpringerLink
Log in

Application of Inductive Bayesian Hierarchical Clustering Algorithm to Identify Brain Tumors

  • Conference paper
  • First Online:
Lecture Notes in Computational Intelligence and Decision Making (ISDMCI 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1246))

  • 503 Accesses

Abstract

The article presents the results of research concerning development of inductive algorithm for hierarchical Bayesian clustering of gene expression of patients with two types of brain tumors and healthy individuals. The study carried out comparative studies of the clustering quality of inductive and classical methods of Bayesian hierarchical clustering algorithm (BHC). It is proposed to apply the moving average and FFT filtering methods for data dimension reducing. The basic principles of creating an inductive model of objective clustering are formed, the results of clustering are shown at various levels of data dimension reducing, the advantages of objective clustering BHC in comparison with the canonical BHC algorithm are determined.

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 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
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. Babichev, S., Taif, M., Lytvynenko, V.: Estimation of the inductive model of objects clustering stability based on the k-means algorithm for different levels of data noise. Radio Electron. Comput. Sci. Control 4, 54–60 (2016). https://doi.org/10.15588/1607-3274-2016-4-7

    Article  Google Scholar 

  2. Bezdek, J., Dunn, J.: Optimal fuzzy partitions: a heuristic for estimating the parameters in a mixture of normal distributions. IEEE Trans. Comput. 100, 835–838 (1975). https://doi.org/10.1109/T-C.1975.224317

    Article  MATH  Google Scholar 

  3. Bleeker, F., Molenaar, R., Leenstra, S.: Recent advances in the molecular understanding of glioblastoma. J. Neurooncol. 108(1), 11–27 (2012). https://doi.org/10.1007/s11060-011-0793-0

    Article  Google Scholar 

  4. Bredel, M., Bredel, C., Juric, D., et al.: Functional network analysis reveals extended gliomagenesis pathway maps and three novel MYC-interacting genes in human gliomas. Cancer Res. 65(19), 8679–8689 (2005). https://doi.org/10.1158/0008-5472.can-05-1204

    Article  Google Scholar 

  5. Brigham, O.: The Fast Fourier Transform and its applications. In: Prentice-Hall Signal Processing Series, Englewood Cliffs, New Jersey (1998)

    Google Scholar 

  6. Calinski, T., Harabasz, J.: A dendrite method for cluster analysis. Commun. Stat. 3, 1–27 (1974). https://doi.org/10.1080/03610927408827101

    Article  MathSciNet  MATH  Google Scholar 

  7. Celebi, M., Kingravi, H., Vela, P.: A comparative study of efficient initialization methods for the k-means clustering algorithm. Expert Syst. Appl. 40(1), 200–210 (2013). https://doi.org/10.1016/j.eswa.2012.07.021

    Article  Google Scholar 

  8. Cha, Y., Park, S., You, R., Kim, H., Yoon, D.: Microstructure arrays of dna using topographic control. Nat. Commun. 10(1), 2512 (2019). https://doi.org/10.1038/s41467-019-10540-2

    Article  Google Scholar 

  9. Chowdhary, S., Chamberlain, M.: Oligodendroglial tumors. Expert Rev. Neurother. 6(4), 519–532 (2006). https://doi.org/10.1586/14737175.6.4.519

    Article  Google Scholar 

  10. Darkins, R., Cooke, E., Ghahramani, Z., et al.: Accelerating Bayesian hierarchical clustering of time series data with a randomised algorithm. PLoS ONE 8(4), e59795 (2013). https://doi.org/10.1371/journal.pone.0059795

    Article  Google Scholar 

  11. Frattini, V., Trifonov, V., Chan, J.: The integrated landscape of driver genomic alterations in glioblastoma. Nat. Genet. 45(10), 1141–1149 (2013)

    Article  Google Scholar 

  12. Garza-Ulloa, J.: Methods to develop mathematical models: traditional statistical analysis. In: Applied Biomechatronics using Mathematical Models, pp. 239–371 (2018). https://doi.org/10.1016/B978-0-12-812594-6.00005-6

  13. Gopinathan, S., Kokila, R., Thangavel, P.: Wavelet and FFT based image denoising using non-linear filters. Int. J. Electr. Comput. Eng. (IJECE) 5(5), 1018–1026 (2015). https://doi.org/10.11591/ijece.v5i5

    Article  Google Scholar 

  14. Heller, K., Ghahramani, Z.: Bayesian hierarchical clustering. In: Proceedings of the 22nd International Conference on Machine Learning, pp. 297–304 (2005). https://doi.org/10.1145/1102351.1102389

  15. Ivakhnenko, A.: The group method of data handling - a rival method of stochastic approximation. Soviet Autom. Control 1, 43–55 (1968)

    Google Scholar 

  16. Ivakhnenko, A.: Group method of data handling as competitor to the method of stochastic approximation. Soviet Autom. Control 3, 64–78 (1968)

    Google Scholar 

  17. Kaufman, L., Rousseeuw, P.: Finding Groups in Data. An Introduction to Cluster Analysis. Wiley, Hoboken (2005). https://doi.org/10.1002/9780470316801

    Book  MATH  Google Scholar 

  18. Lian, B., Hu, X., Shao, Z.M.: Unveiling novel targets of paclitaxel resistance by single molecule long-read RNA sequencing in breast cancer. Sci. Rep. 9(1), 6032 (2019). https://doi.org/10.1038/s41598-019-42184-z

    Article  Google Scholar 

  19. Lowing, N., Bomalaski, R., Mitra, D.: Bayesian Hierarchical Clustering. Nicholas Lowing & Ryan Bomalaski Group 3 CSE 5290 Dr. (2017)

    Google Scholar 

  20. Madala, H., Ivakhnenko, A.: Inductive Learning Algorithms for Complex Systems Modeling. CRC Press, Boco Raton (1994). https://doi.org/10.1201/9781351073493

    Book  MATH  Google Scholar 

  21. Molugaram, K., Rao, S.: Statistical Techniques for Transportation Engineering. Butterworth-Heinemann, Oxford (2017). https://doi.org/10.3846/20294913.2016.1216906

    Book  Google Scholar 

  22. Network, T.: Erratum: corrigendum: comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature 494(7438), 506–506 (2013). https://doi.org/10.1038/nature11903

    Article  Google Scholar 

  23. Omran, M., Engelbrecht, A., Salman, A.: An overview of clustering methods. Intell. Data Anal. 11(6), 583–605 (2007)

    Article  Google Scholar 

  24. Osypenko, V., Reshetjuk, V.: The methodology of inductive system analysis as a tool of engineering researches analytical planning. Ann. Warsaw Univ. Life Sci.-SGGW 58, 67–71 (2011)

    Google Scholar 

  25. Raudys, A., Pabarškaitė, Z.: Optimising the smoothness and accuracy of moving average for stock price data. Technol. Econ. Dev. Econ. 24(3), 984–1003 (2018). https://doi.org/10.1016/B978-0-12-811555-8.00012-X

    Article  Google Scholar 

  26. Sarycheva, L.: Objective cluster analysis of the data on the basis of the group method of data handling. Prob. Manag. Inform. 2, 86–104 (2008)

    MathSciNet  Google Scholar 

  27. Savage, R., Heller, K., Xu, Y., et al.: R/BHC: fast Bayesian hierarchical clustering for microarray data. BMC Bioinform. 10(1), 242 (2009). https://doi.org/10.1186/1471-2105-10-242

    Article  Google Scholar 

  28. Sripada, S., Sreenivasa-Rao, M.: Comparison of purity and entropy of k-means clustering and fuzzy c means clustering. Indian J. Comput. Sci. Eng. 2(3), 343–346 (2011)

    Google Scholar 

  29. Stepashko, V.: Elements of Inductive Modeling Theory - State and Prospects of Informatics Development in Ukraine: Monographic arts. K.: Scientific Thought (2010). https://doi.org/10.15407/kvt194.04.041

  30. Verhaak, R., Hoadley, K., Purdom, E.: An integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR and NF1. Cancer Cell 17(1), 98–110 (2010). https://doi.org/10.1016/j.ccr.2009.12.020

    Article  Google Scholar 

  31. Xu, T., Le, T., Liu, L.: Identifying cancer subtypes from mirna-tf-mrna regulatory networks and expression data. PLoS ONE 11(4), e015279 (2016)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Kherson National Technical University, Kherson, Ukraine

    Iryna Lurie, Volodymyr Lytvynenko, Mariia Voronenko, Oleg Boskin & Michael Sherstiuk

  2. Taras Shevchenko National University, Kyiv, Ukraine

    Serge Olszewski

  3. Lublin University of Technology, Lublin, Poland

    Waldemar Woicik

  4. Astana Medical University, Astana, Kazakhstan

    Ulzhalgas Zhunissova

Authors
  1. Iryna Lurie
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Volodymyr Lytvynenko
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Serge Olszewski
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mariia Voronenko
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Waldemar Woicik
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Oleg Boskin
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Ulzhalgas Zhunissova
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Michael Sherstiuk
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Volodymyr Lytvynenko .

Editor information

Editors and Affiliations

  1. Department of Informatics, Jan Evangelista Purkyně University in Ústí nad Labem, Ústí nad Labem, Czech Republic

    Sergii Babichev

  2. Department of Informatics and Computer Science, Kherson National Technical University, Kherson, Ukraine

    Volodymyr Lytvynenko

  3. Institute of Electronics and Information, Lublin University of Technology, Lublin, Poland

    Waldemar Wójcik

  4. Department of Informatics and Computer Science, Kherson National Technical University, Kherson, Ukraine

    Svetlana Vyshemyrskaya

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Lurie, I. et al. (2021). Application of Inductive Bayesian Hierarchical Clustering Algorithm to Identify Brain Tumors. In: Babichev, S., Lytvynenko, V., Wójcik, W., Vyshemyrskaya, S. (eds) Lecture Notes in Computational Intelligence and Decision Making. ISDMCI 2020. Advances in Intelligent Systems and Computing, vol 1246. Springer, Cham. https://doi.org/10.1007/978-3-030-54215-3_36

Download citation

Publish with us

Policies and ethics

Search

Navigation