Abstract
The paper presents MRNET, an original method for inferring genetic networks from microarray data. The method is based on maximum relevance/minimum redundancy (MRMR), an effective information-theoretic technique for feature selection in supervised learning. The MRMR principle consists in selecting among the least redundant variables the ones that have the highest mutual information with the target. MRNET extends this feature selection principle to networks in order to infer gene-dependence relationships from microarray data. The paper assesses MRNET by benchmarking it against RELNET, CLR, and ARACNE, three state-of-the-art information-theoretic methods for large (up to several thousands of genes) network inference. Experimental results on thirty synthetically generated microarray datasets show that MRNET is competitive with these methods.
Highlights
Two important issues in computational biology are the extent to which it is possible to model transcriptional interactions by large networks of interacting elements and how these interactions can be effectively learned from measured expression data [1]
This paper introduces an original information-theoretic method, called MINIMUM REDUNDANCY NETWORKS (MRNET), inspired by a recently proposed feature selection technique, the maximum relevance/minimum redundancy (MRMR) algorithm [11, 12]
We propose to infer a network using the maximum relevance/minimum redundancy (MRMR) feature selection method
Summary
Two important issues in computational biology are the extent to which it is possible to model transcriptional interactions by large networks of interacting elements and how these interactions can be effectively learned from measured expression data [1]. An additional problem is that by focusing only on transcript data, the inferred network should not be considered as a biochemical regulatory network but as a gene-to-gene network, where many physical connections between macromolecules might be hidden by shortcuts In spite of these evident limitations, the bioinformatics community made important advances in this domain over the last few years. This paper will focus on information-theoretic approaches [3,4,5,6] which typically rely on the estimation of mutual information from expression data in order to measure the statistical dependence between variables (the terms “variable” and “feature” are used interchangeably in this paper) Such methods have recently held the attention of the bioinformatics community for the inference of very large networks [4,5,6]
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