Meyer Patrick E, Lafitte Frédéric, Bontempi Gianluca
Machine Learning Group, Computer Science Department, Faculty of Science, Université Libre de Bruxelles, 1050 Brussels, Belgium.
BMC Bioinformatics. 2008 Oct 29;9:461. doi: 10.1186/1471-2105-9-461.
This paper presents the R/Bioconductor package minet (version 1.1.6) which provides a set of functions to infer mutual information networks from a dataset. Once fed with a microarray dataset, the package returns a network where nodes denote genes, edges model statistical dependencies between genes and the weight of an edge quantifies the statistical evidence of a specific (e.g transcriptional) gene-to-gene interaction. Four different entropy estimators are made available in the package minet (empirical, Miller-Madow, Schurmann-Grassberger and shrink) as well as four different inference methods, namely relevance networks, ARACNE, CLR and MRNET. Also, the package integrates accuracy assessment tools, like F-scores, PR-curves and ROC-curves in order to compare the inferred network with a reference one.
The package minet provides a series of tools for inferring transcriptional networks from microarray data. It is freely available from the Comprehensive R Archive Network (CRAN) as well as from the Bioconductor website.
本文介绍了R/Bioconductor软件包minet（版本1.1.6），它提供了一组从数据集中推断互信息网络的函数。一旦输入一个微阵列数据集，该软件包就会返回一个网络，其中节点表示基因，边对基因之间的统计依赖性进行建模，边的权重量化了特定（如转录）基因到基因相互作用的统计证据。minet软件包提供了四种不同的熵估计器（经验估计器、Miller-Madow估计器、Schurmann-Grassberger估计器和收缩估计器）以及四种不同的推断方法，即相关网络、ARACNE、CLR和MRNET。此外，该软件包还集成了准确性评估工具，如F分数、PR曲线和ROC曲线，以便将推断出的网络与参考网络进行比较。
minet软件包提供了一系列用于从微阵列数据推断转录网络的工具。它可从综合R存档网络（CRAN）以及Bioconductor网站免费获取。
