Advertisement

A Scalable Biclustering Method for Heterogeneous Medical Data

  • Maxence VandrommeEmail author
  • Julie Jacques
  • Julien Taillard
  • Laetitia Jourdan
  • Clarisse Dhaenens
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10122)

Abstract

We define the problem of biclustering on heterogeneous data, that is, data of various types (binary, numeric, etc.). This problem has not yet been investigated in the biclustering literature. We propose a new method, HBC (Heterogeneous BiClustering), designed to extract biclusters from heterogeneous, large-scale, sparse data matrices. The goal of this method is to handle medical data gathered by hospitals (on patients, stays, acts, diagnoses, prescriptions, etc.) and to provide valuable insight on such data. HBC takes advantage of the data sparsity and uses a constructive greedy heuristic to build a large number of possibly overlapping biclusters. The proposed method is successfully compared with a standard biclustering algorithm on small-size numeric data. Experiments on real-life data sets further assert its scalability and efficiency.

Keywords

Heterogeneous Data Data Mining Task Biclustering Algorithm High Sparsity Biological Data Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Bozdağ, D., Kumar, A.S., Catalyurek, U.V.: Comparative analysis of biclustering algorithms. In: Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology, pp. 265–274. ACM (2010)Google Scholar
  2. 2.
    Buluc, A., Fineman, J.T., Frigo, M., Gilbert, J.R., Leiserson, C.E.: Parallel sparse matrix-vector and matrix-transpose-vector multiplication using compressed sparse blocks. In: SPAA, pp. 233–244 (2009)Google Scholar
  3. 3.
    Busygin, S., Prokopyev, O., Pardalos, P.M.: Biclustering in data mining. Comput. Oper. Res. 35(9), 2964–2987 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Cheng, Y., Church, G.M.: Biclustering of expression data. ISMB 8, 93–103 (2000)Google Scholar
  5. 5.
    Dhillon, I.S.: Co-clustering documents and words using bipartite spectral graph partitioning. In: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 269–274. ACM (2001)Google Scholar
  6. 6.
    Henriques, R., Madeira, S.C.: BicNET: flexible module discovery in large-scale biological networks using biclustering. Algorithms Mol. Biol. 11(1), 1 (2016)CrossRefGoogle Scholar
  7. 7.
    Jacques, J., Taillard, J., Delerue, D., Dhaenens, C., Jourdan, L.: Conception of a dominance-based multi-objective local search in the context of classification rule mining in large and imbalanced data sets. Appl. Soft Comput. 34, 705–720 (2015)CrossRefGoogle Scholar
  8. 8.
    Pontes, B., Giráldez, R., Aguilar-Ruiz, J.S.: Biclustering on expression data: a review. J. Biomed. Inform. 57, 163–180 (2015)CrossRefGoogle Scholar
  9. 9.
    Tanay, A., Sharan, R., Kupiec, M., Shamir, R.: Revealing modularity and organization in the yeast molecular network by integrated analysis of highly heterogeneous genomewide data. Proc. Natl. Acad. Sci. U.S.A. 101(9), 2981–2986 (2004)CrossRefGoogle Scholar
  10. 10.
    van Uitert, M., Meuleman, W., Wessels, L.: Biclustering sparse binary genomic data. J. Comput. Biol. 15(10), 1329–1345 (2008)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Yang, J., Wang, W., Wang, H., Yu, P.: \(\delta \)-clusters: capturing subspace correlation in a large data set. In: Proceedings of the 18th International Conference on Data Engineering, pp. 517–528. IEEE (2002)Google Scholar
  12. 12.
    Zhou, J., Khokhar, A.: ParRescue: scalable parallel algorithm and implementation for biclustering over large distributed datasets. In: 26th IEEE International Conference on Distributed Computing Systems, ICDCS 2006, pp. 21–21. IEEE (2006)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Maxence Vandromme
    • 1
    • 2
    • 3
    Email author
  • Julie Jacques
    • 1
  • Julien Taillard
    • 1
  • Laetitia Jourdan
    • 2
    • 3
  • Clarisse Dhaenens
    • 2
    • 3
  1. 1.AlicanteSeclinFrance
  2. 2.CRIStALUMR 9189, University of Lille, CNRS, Centrale LilleVilleneuve d’ascqFrance
  3. 3.INRIA Lille - Nord EuropeVilleneuve d’ascqFrance

Personalised recommendations