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Towards a Classification Approach using Meta-Biclustering: Impact of Discretization in the Analysis of Expression Time Series

Carreiro, André V. ; Ferreira, Artur J. ; Figueiredo, Mário A. T. ; Madeira, Sara C.

Journal of integrative bioinformatics, 2012-12, Vol.9 (3), p.105-120 [Periódico revisado por pares]

Germany: IMBio e.V

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  • Título:
    Towards a Classification Approach using Meta-Biclustering: Impact of Discretization in the Analysis of Expression Time Series
  • Autor: Carreiro, André V. ; Ferreira, Artur J. ; Figueiredo, Mário A. T. ; Madeira, Sara C.
  • Assuntos: Algorithms ; Cluster Analysis ; Computational Biology - methods ; Gene Expression Regulation ; Humans ; Time Factors ; Workflow
  • É parte de: Journal of integrative bioinformatics, 2012-12, Vol.9 (3), p.105-120
  • Notas: ObjectType-Article-1
    SourceType-Scholarly Journals-1
    ObjectType-Feature-2
    content type line 23
  • Descrição: Biclustering has been recognized as a remarkably effective method for discovering local temporal expression patterns and unraveling potential regulatory mechanisms, essential to understanding complex biomedical processes, such as disease progression and drug response. In this work, we propose a classification approach based on meta-biclusters (a set of similar biclusters) applied to prognostic prediction. We use real clinical expression time series to predict the response of patients with multiple sclerosis to treatment with Interferon-β. As compared to previous approaches, the main advantages of this strategy are the interpretability of the results and the reduction of data dimensionality, due to biclustering. This would allow the identification of the genes and time points which are most promising for explaining different types of response profiles, according to clinical knowledge. We assess the impact of different unsupervised and supervised discretization techniques on the classification accuracy. The experimental results show that, in many cases, the use of these discretization methods improves the classification accuracy, as compared to the use of the original features.
  • Editor: Germany: IMBio e.V
  • Idioma: Inglês
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