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Dejean, S., Martin, P.G., Baccini, A. and Besse, P. (2007) Clustering time-series gene expression data using smoothing spline deriv-atives. EURASIP Journal on Bio-informatics & Systems Biology, 2007, Article ID: 70561.

has been cited by the following article:

  • TITLE: Fuzzy clustering of time series gene expression data with cubic-spline

    AUTHORS: Yu Wang, Maia Angelova, Akhtar Ali

    KEYWORDS: Fuzzy c-Means; Cubic Spline; Noise; Radius of Curvature

    JOURNAL NAME: Journal of Biosciences and Medicines, Vol.1 No.3, December 12, 2013

    ABSTRACT: Data clustering techniques have been applied to ex- tract information from gene expression data for two decades. A large volume of novel clustering algorithms have been developed and achieved great success. However, due to the diverse structures and intensive noise, there is no reliable clustering approach can be applied to all gene expression data. In this paper, we aim to the feature of high noise and propose a cubic smoothing spline fitted for the time course ex- pression profile, by which noise can be filtered and then groups genes into clusters by applying fuzzy c-means clustering on the resulting splines (FCMS). The discrete values of radius of curvature are used to compute the similarity between spline curves. Results on gene expression data show that the FCMS has better performance than the original fuzzy c-means on reliability and noise robustness.