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

Wang, Yu, Angelova, Maia and Ali, Akhtar (2013) Fuzzy clustering of time series gene expression data with cubic-spline. Journal of Biosciences and Medicines, 1 (3). pp. 16-21. ISSN 2327-5081

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Official URL: http://dx.doi.org/10.4236/jbm.2013.13004


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.

Item Type: Article
Uncontrolled Keywords: fuzzy c-means, cubic spline, noise, radius of curvature
Subjects: C100 Biology
G100 Mathematics
G400 Computer Science
Department: Faculties > Engineering and Environment > Mathematics, Physics and Electrical Engineering
Depositing User: Maia Angelova
Date Deposited: 04 Jul 2014 12:45
Last Modified: 17 Dec 2023 15:21
URI: https://nrl.northumbria.ac.uk/id/eprint/16835

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