Bayesian biclustering of gene expression data
نویسندگان
چکیده
منابع مشابه
Bayesian Biclustering of Gene Expression
Background: Biclustering of gene expression data searches for local patterns of gene expression. A bicluster (or a two-way cluster) is defined as a set of genes whose expression profiles are mutually similar within a subset of experimental conditions/samples. Although several biclustering algorithms have been studied, few are based on rigorous statistical models. Results: We developed a Bayesia...
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Identifying latent structure in large data matrices is essential for exploring biological processes. Here, we consider recovering gene co-expression networks from gene expression data, where each network encodes relationships between genes that are locally co-regulated by shared biological mechanisms. To do this, we develop a Bayesian statistical model for biclustering to infer subsets of co-re...
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BACKGROUND Accumulated biological research outcomes show that biological functions do not depend on individual genes, but on complex gene networks. Microarray data are widely used to cluster genes according to their expression levels across experimental conditions. However, functionally related genes generally do not show coherent expression across all conditions since any given cellular proces...
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Microarray technology is a powerful method for monitoring the expression level of thousands of genes in parallel. Using this technology, the expression levels of genes are measured. Microarray data is represented in N × M matrix. Each row indicates genes and each column indicates condition. In Gene Expression data, standard clustering algorithms are called as global clustering. In global cluste...
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ژورنال
عنوان ژورنال: BMC Genomics
سال: 2008
ISSN: 1471-2164
DOI: 10.1186/1471-2164-9-s1-s4