minet: A R/Bioconductor Package for Inferring Large Transcriptional Networks Using Mutual Information
By Patrick E. Meyer, Frédéric Lafitte, Gianluca Bontempi. BMC Bioinformatics, 2008
510 citations recorded by the source corpus.
ResultsThis 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.ConclusionThe 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.
Subfields: Molecular Biology
View Paper: https://www.semanticscholar.org/paper/9a4f2cc386bd67a4765138878721811c5e1f37e1
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In Molecular Biology
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Citations in the corpus
- Inferring Regulatory Networks from Expression Data Using Tree-Based Methods (2010, methodology): “For these experiments, we used the original MatLab implementation of CLR [43] and the implementations of ARACNE and MRNET in the minet R package [44].”
- Gene Regulatory Network Inference from Single-Cell Data Using Multivariate Information Measures (2016, methodology): “MRNET (Meyer et al. 2007) aims to identify a minimally redundant but maximally explanatory set of variables/predictors for each target gene X in a greedy manner.”
- The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets (2015, methodology): “Several integrated machine learning and bioinformatics platforms such as WEKA [34] and Bioconductor [42, 43] also have basic functions or libraries for making ROC and PRC plots.”
- Determinants of community structure in the global plankton interactome (2015, methodology): “CI and I were estimated by using minet (81).”