Gene Regulatory Network inference incorporating Maximal Information Coefficient into Minimal Redundancy Network

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Gene Regulatory Network (GRN) plays an important role to understand the interactions and dependencies of genes in different conditions from gene expression data. An information theoretic GRN method first computes dependency matrix from the given gene expression dataset using an entropy estimator and then infer network using individual inference method. A number of prominent methods use Mutual Information (MI) because it is an efficient approach to detect nonlinear dependencies. But MI does not work well for continuous multivariate variables. In this study, we have investigated the recently proposed association detector method Maximal Information Coefficient (MIC) in inferring GRN. We have incorporated MIC into the prominent MI based method Minimal Redundancy Network (MRNET) and proposed MRNET-MIC. The experimental studies on SynTReN generated gene expression data revealed that proposed MRNET-MIC outperformed its counter standard MRNET in GRN inference.

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