Estimating genomic coexpression networks using first-order conditional independence.
Estimating genomic coexpression networks using first-order conditional independence.
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DOI：
10.1186/gb-2004-5-12-r100
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发表时间：
2004
期刊：
影响因子：
12.3
通讯作者：
Kim J
中科院分区：
文献类型：
--
作者：
Magwene PM;Kim J
A computationally efficient statistical framework for estimating networks of coexpressed genes is presented that exploits first-order conditional independence relationships among gene expression measurements. We describe a computationally efficient statistical framework for estimating networks of coexpressed genes. This framework exploits first-order conditional independence relationships among gene-expression measurements to estimate patterns of association. We use this approach to estimate a coexpression network from microarray gene-expression measurements from Saccharomyces cerevisiae. We demonstrate the biological utility of this approach by showing that a large number of metabolic pathways are coherently represented in the estimated network. We describe a complementary unsupervised graph search algorithm for discovering locally distinct subgraphs of a large weighted graph. We apply this algorithm to our coexpression network model and show that subgraphs found using this approach correspond to particular biological processes or contain representatives of distinct gene families.
