Format

Send to

Choose Destination
BMC Bioinformatics. 2008 Oct 29;9:461. doi: 10.1186/1471-2105-9-461.

minet: A R/Bioconductor package for inferring large transcriptional networks using mutual information.

Author information

1
Machine Learning Group, Computer Science Department, Faculty of Science, Université Libre de Bruxelles, 1050 Brussels, Belgium. pmeyer@ulb.ac.be

Abstract

RESULTS:

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.

CONCLUSION:

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.

PMID:
18959772
PMCID:
PMC2630331
DOI:
10.1186/1471-2105-9-461
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for BioMed Central Icon for PubMed Central
Loading ...
Support Center