An Entropy-Based Graph Construction Method for Representing and Clustering Biological Data

  • Leandro Ariza-JiménezEmail author
  • Nicolás Pinel
  • Luisa F. Villa
  • Olga Lucía Quintero
Conference paper
Part of the IFMBE Proceedings book series (IFMBE, volume 75)


Unsupervised learning methods are commonly used to perform the non-trivial task of uncovering structure in biological data. However, conventional approaches rely on methods that make assumptions about data distribution and reduce the dimensionality of the input data. Here we propose the incorporation of entropy related measures into the process of constructing graph-based representations for biological datasets in order to uncover their inner structure. Experimental results demonstrated the potential of the proposed entropy-based graph data representation to cope with biological applications related to unsupervised learning problems, such as metagenomic binning and neuronal spike sorting, in which it is necessary to organize data into unknown and meaningful groups.


Entropy Graph Clustering Biological data Spike sorting Metagenomic binning. 



This research was supported by Centro de Excelencia y Apropiación en Big Data y Data Analytics -Alianza CAOBA- and Universidad EAFIT.

Conflict of Interest

The authors declare that they have no conflicts of interest.


  1. 1.
    Vogt, J.E.: Unsupervised structure detection in biomedical data. IEEE/ACM Trans. Comput. Biol. Bioinforma. 12(4), 753–760 (2015). Scholar
  2. 2.
    Xu, R., Wunsch, D.C.: Clustering algorithms in biomedical research: a review. IEEE Rev. Biomed. Eng. 3, 120–154 (2010). Scholar
  3. 3.
    Fortunato, S., Hric, D.: Community detection in networks: a user guide. Phys. Rep. 659, 1–44 (2016). Scholar
  4. 4.
    de Arruda, G.F., Costa, L.D.F., Rodrigues, F.A.: A complex networks approach for data clustering. Phys. A Stat. Mech. Appl. 391(23), 6174–6183 (2012). Scholar
  5. 5.
    Zhang, H., Chen, X.: Network-based clustering and embedding for high-dimensional data visualization. In: 2013 International Conference on Computer-Aided Design and Computer Graphics, pp. 290–297. IEEE (2013).
  6. 6.
    Grünwald, P.D.: The Minimum Description Length Principle. The MIT Press, Cambridge (2007)CrossRefGoogle Scholar
  7. 7.
    Yao, J., Dash, M., Tan, S.T., Liu, H.: Entropy-based fuzzy clustering and fuzzy modeling. Fuzzy Sets Syst. 113(3), 381–388 (2000). Scholar
  8. 8.
    Laskaris, N.A., Zafeiriou, S.P.: Beyond FCM: graph-theoretic post-processing algorithms for learning and representing the data structure. Pattern Recognit. 41(8), 2630–2644 (2008). Scholar
  9. 9.
    Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech. Theory Exp. 2008(10), P10008 (2008). Scholar
  10. 10.
    Leung, H.C., Yiu, S.M., Yang, B., Peng, Y., Wang, Y., Liu, Z., Chen, J., Qin, J., Li, R., Chin, F.Y.: A robust and accurate binning algorithm for metagenomic sequences with arbitrary species abundance ratio. Bioinformatics 27(11), 1489–1495 (2011). Scholar
  11. 11.
    Ceballos, J., Ariza-Jiménez, L., Pinel, N.: Standardized approaches for assessing metagenomic contig binning performance from Barnes-Hut \(t\)-Stochastic neighbor embeddings. In: VIII Latin American Conference on Biomedical Engineering and XLII National Conference on Biomedical Engineering, pp. 761–768. Springer Nature Switzerland AG (2020).
  12. 12.
    Ariza-Jiménez, L., Quintero, O., Pinel, N.: Unsupervised fuzzy binning of metagenomic sequence fragments on three-dimensional Barnes-Hut t-Stochastic neighbor embeddings. In: 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 1315–1318. IEEE (2018).
  13. 13.
    van der Maaten, L.: Accelerating t-SNE using tree-based algorithms. J. Mach. Learn. Res. 15, 3221–3245 (2014). Scholar
  14. 14.
    Chaure, F.J., Rey, H.G., Quian Quiroga, R.: A novel and fully automatic spike-sorting implementation with variable number of features. J. Neurophysiol. 120(4), 1859–1871 (2018). Scholar
  15. 15.
    Pedreira, C., Martinez, J., Ison, M.J., Quian Quiroga, R.: How many neurons can we see with current spike sorting algorithms? J. Neurosci. Methods 211(1), 58–65 (2012). Scholar
  16. 16.
    Newman, M.E., Clauset, A.: Structure and inference in annotated networks. Nat. Commun. 7(May), 1–11 (2016). Scholar
  17. 17.
    Sharon, I., Morowitz, M.J., Thomas, B.C., Costello, E.K., Relman, D.A., Banfield, J.F.: Time series community genomics analysis reveals rapid shifts in bacterial species, strains, and phage during infant gut colonization. Genome Res. 23(1), 111–120 (2013). Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Mathematical Modeling Research GroupUniversidad EAFITMedellí­nColombia
  2. 2.Biodiversity, Evolution, and Conservation Research GroupUniversidad EAFITMedellí­nColombia
  3. 3.System Engineering Research Group, ARKADIUSUniversidad de Medellí­nMedellí­nColombia

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