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• W2109153921 abstract "Clustering is an important data processing tool for interpreting microarray data and genomic network inference. In this article, we propose a clustering algorithm based on the hierarchical Dirichlet processes (HDP). The HDP clustering introduces a hierarchical structure in the statistical model which captures the hierarchical features prevalent in biological data such as the gene express data. We develop a Gibbs sampling algorithm based on the Chinese restaurant metaphor for the HDP clustering. We apply the proposed HDP algorithm to both regulatory network segmentation and gene expression clustering. The HDP algorithm is shown to outperform several popular clustering algorithms by revealing the underlying hierarchical structure of the data. For the yeast cell cycle data, we compare the HDP result to the standard result and show that the HDP algorithm provides more information and reduces the unnecessary clustering fragments." @default.
• W2109153921 created "2016-06-24" @default.
• W2109153921 creator A5025174368 @default.
• W2109153921 creator A5058722770 @default.
• W2109153921 date "2013-04-12" @default.
• W2109153921 modified "2023-09-23" @default.
• W2109153921 title "Hierarchical Dirichlet process model for gene expression clustering" @default.
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• W2109153921 doi "https://doi.org/10.1186/1687-4153-2013-5" @default.
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