Reconstruction of gene network through Backward Elimination based Information-Theoretic Inference with Maximal Information Coefficient

For understanding the complex processes of regulation within the system of cellular and every process of life in different developmental and environmental contexts, reconstructing Gene Regulatory Networks(GRNs) is an essential part of Systems Biology. A recently developed maximal information coefficient (MIC) is better to detect all kinds of association than others and it maintains both generality and equitability properties. In this study, we combined MIC as an entropy estimator with gene regulatory network method Backward Elimination based Information-Theoretic Inference and then compare this proposed method with the MI-based algorithm MRNETB by examining SynTReN's datasets. The performance of our proposed MIC based MRNETB (MRNETB-MIC) is given by using both the receiver operator characteristic (ROC) curve and the precision-recall (PR) curve and from these, the proposed method shows significantly better performance in reconstructing gene regulatory network.