mrnet: Maximum Relevance Minimum Redundancy

In minet: Mutual Information NETworks

Description

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.

Usage

mrnet(mim)

Arguments

mim	A square matrix whose i,j th element is the mutual information between variables X_i and X_j - see build.mim.

Details

The MRNET approach consists in repeating a MRMR feature selection procedure for each variable of the dataset. The MRMR method starts by selecting the variable Xi having the highest mutual information with the target Y. In the following steps, given a set S of selected variables, the criterion updates S by choosing the variable Xk that maximizes I(Xk;Y) - mean(I(Xk;Xi)), Xi in S.
The weight of each pair Xi,Xj will be the maximum score between the one computed when Xi is the target and the one computed when Xj is the target.

Value

mrnet returns a matrix which is the weighted adjacency matrix of the network. In order to display the network, load the package Rgraphviz and use the following command:
plot( as( returned.matrix ,"graphNEL") )

Author(s)

Patrick E. Meyer, Frederic Lafitte, Gianluca Bontempi

References

Patrick E. Meyer, Kevin Kontos, Frederic Lafitte and Gianluca Bontempi. Information-theoretic inference of large transcriptional regulatory networks. EURASIP Journal on Bioinformatics and Systems Biology, 2007.

Patrick E. Meyer, Frederic Lafitte and Gianluca Bontempi. minet: A R/Bioconductor Package for Inferring Large Transcriptional Networks Using Mutual Information. BMC Bioinformatics, Vol 9, 2008.

H. Peng, F.long and C.Ding. Feature selection based on mutual information: Criteria of max-dependency, max relevance and min redundancy. IEEE transaction on Pattern Analysis and Machine Intelligence, 2005.

See Also

build.mim, clr, aracne, mrnetb

Examples

data(syn.data)
mim <- build.mim(syn.data, estimator="spearman")
net <- mrnet(mim)

Example output

minet documentation built on Nov. 8, 2020, 6 p.m.