Reconstructing directed gene regulatory network by only gene expression data

Accurately identifying gene regulatory network serves an important task in understanding in vivo biological activities. The inference of such network is often accomplished through the use of gene expression data. Some methods further predict the regulatory directions in the network by using the location of eQTL single nucleotide polymorphisms, or through gene knock out/down experiments; regrettably, these additional data are not always available, especially for the samples deriving from human tissues. In this paper, we propose Context Based Dependency Network (CBDN), a method that is able to infer gene regulatory networks, complete with the regulatory directions, from only gene expression data. CBDN applies directed data processing inequality (DDPI) to distinguish between direct and transitive relationship between genes. In our experiments with simulated and real data, CBDN outperforms the current state-of-the-art approaches. When used to identify important regulators in a network, CBDN 1. correctly identified TYROBP in the network related to Alzheimer's disease; 2. predicted potential important regulators ZNF329 and RB1 for human brain tumors.