A data-driven clustering method for time course gene expression data

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A data-driven clustering method for time course gene expression data

Gene expression over time is, biologically, a continuous process and can thus be represented by a continuous function, i.e. a curve. Individual genes often share similar expression patterns (functional forms). However, the shape of each function, the number of such functions, and the genes that share similar functional forms are typically unknown. Here we introduce an approach that allows direc...

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Model-Driven Clustering of Time-Course Gene Expression Data

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Clustering of Time-Course Gene Expression Data

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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Jackknife distances for clustering time–course gene expression data

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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Approaches to clustering gene expression time course data

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: Nucleic Acids Research

سال: 2006

ISSN: 0305-1048,1362-4962

DOI: 10.1093/nar/gkl013