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使用MMG识别差异表达的子网。

Identifying differentially expressed subnetworks with MMG.

作者信息

Noirel Josselin, Sanguinetti Guido, Wright Phillip C

机构信息

Biological and Environmental Systems Group, Department of Chemical and Process Engineering, University 10 of Sheffield, Mappin Street, Sheffield, UK.

出版信息

Bioinformatics. 2008 Dec 1;24(23):2792-3. doi: 10.1093/bioinformatics/btn499. Epub 2008 Sep 25.

Abstract

BACKGROUND

Mixture model on graphs (MMG) is a probabilistic model that integrates network topology with (gene, protein) expression data to predict the regulation state of genes and proteins. It is remarkably robust to missing data, a feature particularly important for its use in quantitative proteomics. A new implementation in C and interfaced with R makes MMG extremely fast and easy to use and to extend.

AVAILABILITY

The original implementation (Matlab) is still available from http://www.dcs.shef.ac.uk/~guido/; the new implementation is available from http://wrightlab.group.shef.ac.uk/people_noirel.htm, from CRAN, and has been submitted to BioConductor, http://www.bioconductor.org/.

摘要

背景

图上混合模型(MMG)是一种概率模型,它将网络拓扑结构与(基因、蛋白质)表达数据相结合,以预测基因和蛋白质的调控状态。它对缺失数据具有很强的鲁棒性,这一特性在其用于定量蛋白质组学中尤为重要。用C语言实现并与R语言接口的新方法使得MMG极其快速且易于使用和扩展。

可用性

原始实现(Matlab)仍可从http://www.dcs.shef.ac.uk/~guido/获取;新实现可从http://wrightlab.group.shef.ac.uk/people_noirel.htm、CRAN获取,并且已提交至BioConductor,网址为http://www.bioconductor.org/。

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