Causality inference techniques for in-silico gene regulatory network

Abstract

Causality detection in gene regulatory networks (GRN) is a challenging problem due to the limit of available data and lack of efficiency in the existing techniques. A number of techniques proposed so far to reconstruct GRN. However, majority of them ignore drawing causality among genes which indicates regulatory relationship. In this paper, we study few techniques available for inferring causality. We select four state-of-the-art causality detection techniques namely, Bayesian network, Granger causality, Mutual information(MI) and Transfer entropy based approach for our study. Performance of the techniques are evaluated using DREAM challenge data based on associated in-silico regulatory networks. Experimental results reveal the superiority of MI based approach in terms of prediction accuracy in comparison to other techniques. © 2013 Springer International Publishing.