Context likelihood of relatedness with maximal information coefficient for Gene Regulatory Network inference

In this study, we have investigated the recently proposed association detector method Maximal Information Coefficient (MIC) instead of Mutual Information (MI) in inferring Gene Regulatory Network (GRN). 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 MI because it is an efficient approach to detect nonlinear dependencies. But MI does not work well for continuous multivariate variables. In this paper, MIC incorporated into the prominent MI based GRN method Context Likelihood of Relatedness (CLR) and proposed CLR-MIC. To understand the effectiveness of MIC in GRN inference, SynTReN generated synthetic data and SOS E. Coli real gene expression data were considered. The experimental results revealed that proposed CLR-MIC outperformed its counter standard CLR and identified the proficiency of MIC in GRN inference.