Clustering of time-course gene expression data using functional data analysis
نویسندگان
چکیده
منابع مشابه
Clustering of time-course gene expression data using functional data analysis
Clustering of gene expression data collected across time is receiving growing attention in the biological literature since time-course experiments allow one to understand dynamic biological processes and identify genes governed by the same processes. It is believed that genes demonstrating similar expression profiles over time might give an informative insight into how underlying biological mec...
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Microarray experiments have been used to measure genes’ expression levels under different cellular conditions or along certain time course. Initial attempts to interpret these data begin with grouping genes according to similarity in their expression profiles. The widely adopted clustering techniques for gene expression data include hierarchical clustering, self-organizing maps, and K-means clu...
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Anne Badel-Chagnon , Gaëlle Lelandais , Serge Hazout and Pierre Vincens Equipe de Bioinformatique Génomique et Moléculaire, Inserm E0346, Université Paris 7, case 7113, 2 Place Jussieu, 75251 Paris, France Laboratoire de Génétique Moléculaire, CNRS UMR 8541, Ecole Normale Supérieure, 46 rue d’Ulm, 75230 Paris Cedex 05, France Département de Biologie (FR36), Ecole Normale Supérieure, 46 rue d’Ul...
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Clustering is an important tool in microarray data analysis. This unsupervised learning technique is commonly used to reveal structures hidden in large gene expression data sets. The vast majority of clustering algorithms applied so far produce hard partitions of the data, i.e. each gene is assigned exactly to one cluster. Hard clustering is favourable if clusters are well separated. However, t...
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Conventional techniques to cluster gene expression time course data have either ignored the time aspect, by treating time points as independent, or have used parametric models where the model complexity has to be fixed beforehand. In this thesis, we have applied a non-parametric version of the traditional hidden Markov model (HMM), called the hierarchical Dirichlet process hidden Markov model (...
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ژورنال
عنوان ژورنال: Computational Biology and Chemistry
سال: 2007
ISSN: 1476-9271
DOI: 10.1016/j.compbiolchem.2007.05.006