tailieunhanh - Báo cáo y học: "Estimating genomic coexpression networks using first-order conditional independence"

Tuyển tập các báo cáo nghiên cứu về y học được đăng trên tạp chí y học quốc tế cung cấp cho các bạn kiến thức về ngành y đề tài: Estimating genomic coexpression networks using first-order conditional independence. | Metho d Open Access Estimating genomic coexpression networks using first-order conditional independence Paul M Magwene and Junhyong Kim Addresses Department of Biology University of Pennsylvania 415 S University Avenue Philadelphia PA 19104 USA. Current address Department of Biology Duke University Durham NC 27708 USA. Correspondence Paul M Magwene. E-mail Published 30 November 2004 Genome Biology 2004 5 R100 The electronic version of this article is the complete one and can be found online at http 2004Z5 12 R100 Received 28 May 2004 Revised 7 June 2004 Accepted 2 November 2004 2004 Magwene and Kim licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License http licenses by which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. Abstract 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. Background Analyses of functional genomic data such as gene-expression microarray measurements are subject to what has been called the curse of dimensionality .