Information-Theoretic Inference of Large Transcriptional Regulatory Networks

By Patrick E. Meyer, Kevin Kontos, Frédéric Lafitte, Gianluca Bontempi. EURASIP J. Bioinform. Syst. Biol., 2007

462 citations recorded by the source corpus.

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.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/15c8f39da52334ab32cc08777c763f85d5fea884

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Citations in the corpus

Information-Theoretic Feature Selection in Microarray Data Using Variable Complementarity (2008, background): “For instance, in network inference, where the subset of regulator genes is searched, variables such as coregulated genes are meant to be eliminated [30] whereas in a best classification accuracy perspective, dependent variables of the regulator genes can improve the robustness of the classification.”
Data Based Identification and Prediction of Nonlinear and Complex Dynamical Systems (2016, methodology): “Gene Network Inference with Ensemble of Trees (GENIE), similar to MRNET, identifies the best subset of regulator genes with random forest and extra-trees for regression and feature selection instead of MI and MRMR.”
A Theory of Usable Information Under Computational Constraints (2020, methodology): “Additionally, methods based on mutual information cannot take advantage of intrinsically asymmetric relationships, which are common for example in gene regulatory networks (Meyer et al., 2007).”
Gene Regulatory Network Inference from Single-Cell Data Using Multivariate Information Measures (2016, background): “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.”
minet: A R/Bioconductor Package for Inferring Large Transcriptional Networks Using Mutual Information (2008, methodology): “The rationale of this family of methods is to infer a link between a couple of nodes if it has a high score based on mutual information [9].”
Comparison of co-expression measures: mutual information, correlation, and model based indices (2012, methodology): “MRNET MRNET [27] infers a network by repeating the maximum relevance/minimum redundancy (MRMR) feature selection method for all variables.”
Entropy Inference and the James-Stein Estimator, with Application to Nonlinear Gene Association Networks (2008, methodology): “Thus, both the ARACNE and the MRNET algorithms can be considered as devices to approximate the conditional mutual information (Meyer et al., 2007).”
Inferring Regulatory Networks from Expression Data Using Tree-Based Methods (2010, methodology): “Finally, MRNET [14] uses an iterative feature selection method based on a maximum relevance/ minimum redundancy criterion.”