minet (version 3.30.0)

mrnet: Maximum Relevance Minimum Redundancy

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 $Xi$ and $Xj$ - see build.mim.

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") )

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 $X_i$ having the highest mutual information with the target $Y$. In the following steps, given a set $\mathcal{S}$ of selected variables, the criterion updates $\mathcal{S}$ by choosing the variable $X_k$ that maximizes $I(X_k;Y) - \frac{1}{|\mathcal{S}|}\sum_{X_i \in \mathcal{S}} I(X_k;X_i)$ The weight of each pair $X_i,X_j$ will be the maximum score between the one computed when $X_i$ is the target and the one computed when $X_j$ is the target.

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)