Comparative analysis of clustering methods for gene expression time course data
نویسندگان
چکیده
منابع مشابه
Statistical methods for analysis of time course gene expression data.
Since many biological systems or regulatory networks are dynamic systems, gene expression levels measured over different time points during a given biological process can often provide more insights about the underlying system. These gene expression data measured over time are often called the time-course gene expression data. One unique feature of such data is the time dependency of the gene e...
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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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Clustering time–course gene expression data is a common tool to find co–regulated genes and groups of genes with similar temporal or spatial expression patterns. The distance measure used for clustering has major impact on the properties of the resulting clusters. As technical problems can easily distort the microarray data there is a need for distance measures which are able to deal with outli...
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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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ژورنال
عنوان ژورنال: Genetics and Molecular Biology
سال: 2004
ISSN: 1415-4757
DOI: 10.1590/s1415-47572004000400025