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minet: A R/Bioconductor Package for Inferring Large Transcriptional Networks Using Mutual Information

Patrick E Meyer*, Frédéric Lafitte and Gianluca Bontempi

Author Affiliations

Machine Learning Group, Computer Science Department, Faculty of Science, Université Libre de Bruxelles, 1050 Brussels, Belgium

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BMC Bioinformatics 2008, 9:461  doi:10.1186/1471-2105-9-461

Published: 29 October 2008



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.