aracne
(Package: minet) :
Algorithm for the Reconstruction of Accurate Cellular NEtworks
This function takes the mutual information matrix as input in order to return the infered network according to the Aracne algorithm. This algorithm applies the data processing inequality to all triplets of nodes in order to remove the least significant edge in each triplet.
● Data Source:
BioConductor
● Keywords: misc
● Alias: aracne
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build.mim
(Package: minet) :
Build Mutual Information Matrix
build.mim takes the dataset as input and computes the mutual information beetween all pair of variables according to the mutual inforamtion estimator estimator. The results are saved in the mutual information matrix (MIM), a square matrix whose (i,j) element is the mutual information between variables Xi and Xj.
● Data Source:
BioConductor
● Keywords: misc
● Alias: build.mim
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clr
(Package: minet) :
Context Likelihood or Relatedness Network
clr takes the mutual information matrix as input in order to return the infered network - see details.
● Data Source:
BioConductor
● Keywords: misc
● Alias: clr
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minet
(Package: minet) :
Mutual Information Network
For a given dataset, minet infers the network in two steps. First, the mutual information between all pairs of variables in dataset is computed according to the estimator argument. Then the algorithm given by method considers the estimated mutual informations in order to build the network. This package is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License.
● Data Source:
BioConductor
● Keywords: misc
● Alias: minet
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mrnet
(Package: minet) :
Maximum Relevance Minimum Redundancy
mrnet takes the mutual information matrix as input in order to infer the network using the maximum relevance/minimum redundancy feature selection method - see details.
● Data Source:
BioConductor
● Keywords: misc
● Alias: mrnet
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mrnetb
(Package: minet) :
Maximum Relevance Minimum Redundancy Backward
mrnetb takes the mutual information matrix as input in order to infer the network using the maximum relevance/minimum redundancy criterion combined with a backward elimination and a sequential replacement - see references. This method is a variant of mrnet.
● Data Source:
BioConductor
● Keywords: misc
● Alias: mrnetb
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validate
(Package: minet) :
Inference Validation
validate compares the infered network to the true underlying network for several threshold values and appends the resulting confusion matrices to the returned object.
● Data Source:
BioConductor
● Keywords: misc
● Alias: validate
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vis.res
(Package: minet) :
Visualize Results
A group of functions to plot precision-recall and ROC curves and to compute f-scores from the data.frame returned by the validate function.
● Data Source:
BioConductor
● Keywords: misc
● Alias: auc.pr, auc.roc, fscores, pr, rates, show.pr, show.roc, vis.res
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