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Biclustering-Based Classification of Clinical Expression Time Series: A Case Study in Patients with Multiple Sclerosis

  • André V. Carreiro
  • Orlando Anunciação
  • João A. Carriço
  • Sara C. Madeira
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 93)

Abstract

In the last years the constant drive towards a more personalized medicine led to an increasing interest in temporal gene expression analyses. In fact, considering a temporal aspect represents a great advantage to better understand disease progression and treatment results at a molecular level. In this work, we analyse multiple gene expression time series in order to classify the response of Multiple Sclerosis patients to the standard treatment with Interferon-β , to which nearly half of the patients reveal a negative response. In this context, obtaining a highly predictive model of a patient’s response would definitely improve his quality of life, avoiding useless and possibly harmful therapies for the non-responder group. We propose new strategies for time series classification based on biclustering. Preliminary results achieved a prediction accuracy of 94.23% and reveal potentialities to be further explored in classification problems involving other (clinical) time series.

Keywords

Prediction Accuracy Good Responder Score Matrix Expression Time Series Gene Expression Time Series 
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.

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References

  1. 1.
    Baranzini, S., Mousavi, P., Rio, J., Stillman, S.C.A., Villoslada, P., Wyatt, M., Comabella, M., Greller, L., Somogyi, R., Montalban, X., Oksenberg, J.: Transcription-based prediction of response to ifnbeta using supervised computational methods. PLoS Biology 3(1) (2005)Google Scholar
  2. 2.
    Costa, I.G., Schönhuth, A., Hafemeister, C., Schliep, A.: Constrained mixture estimation for analysis and robust classification of clinical time series. Bioinformatics 25(12), i6–i14 (2009)CrossRefGoogle Scholar
  3. 3.
    Hemmer, B., Archelos, J.J., Hartung, H.: New concepts in the immunopathogenesis of multiple sclerosis. Nature Reviews in Neurosciences 3(4), 291–301 (2002)CrossRefGoogle Scholar
  4. 4.
    Lin, T.H., Kaminski, N., Bar-Joseph, Z.: Alignment and classification of time series gene expression in clinical studies. Bioinformatics 24(13), i147–i155 (2008)CrossRefGoogle Scholar
  5. 5.
    Madeira, S.C., Teixeira, M.C., S-Correia, I., Oliveira, A.: Identification of regulatory modules in time series gene expression data using a linear time biclustering algorithm. IEEE/ACM Transactions on Computational Biology and Bioinformatics 7(1), 153–165 (2010)CrossRefGoogle Scholar
  6. 6.
    Sturzebecher, S., Wandinger, K., Rosenwald, A., Sathyamoorthy, M., Tzou, A., Mattar, P., Frank, J., Staudt, L., Martin, R., McFarland, H.: Expression profiling identifies responder and non-responder phenotypes to interferon-beta in multiple sclerosis. Brain 126(6) (2003)Google Scholar
  7. 7.
    Ye, L., Keogh, E.: Time series shapelets: a new primitive for data mining. In: Proc. of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • André V. Carreiro
    • 1
    • 2
  • Orlando Anunciação
    • 1
    • 2
  • João A. Carriço
    • 3
  • Sara C. Madeira
    • 1
    • 2
  1. 1.Knowledge Discovery and Bioinformatics (KDBIO) groupINESC-IDLisbonPortugal
  2. 2.Instituto Superior TécnicoTechnical University of LisbonLisbonPortugal
  3. 3.Molecular Microbiology and Infection Unit, IMM and Faculty of MedicineUniversity of LisbonLisbonPortugal

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