TY - JOUR
AU - Bontempi, Gianluca
AB - 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. Background between all pairs of variables, have recently held the atten- Modelling transcriptional interactions by large networks tion of the bioinformatics community for the inference of of interacting elements and determining how these inter- very large networks (up to several thousands nodes) [4,7- actions can be effectively learned from measured 
TI - minet: A R/Bioconductor Package for Inferring Large Transcriptional Networks Using Mutual Information
JF - BMC Bioinformatics
DO - 10.1186/1471-2105-9-461
DA - 2008-10-29
UR - https://www.deepdyve.com/lp/springer-journals/minet-a-r-bioconductor-package-for-inferring-large-transcriptional-mDQF0rMS8D
SP - 1
EP - 10
VL - 9
IS - 1
DP - DeepDyve
ER -