Gene Regulation Network (GRN) is a graphical representation of the relationship for a collection of regulators that interact with each other and with other substances in the cell to govern the gene expression levels of mRNA and proteins. In this study, we examine the extraction of GRN from literatures using a statistical method. Markovian logic has been used in the natural language processing domain extensively such as in the field of speech recognition. This paper presents an event extraction approach using the Markov’s method and the logical predicates. An event extraction task is modeled into a Markov’s model using the logical predicates and a set of weighted first ordered formulae that defines a distribution of events over a set of ground atoms of the predicates that is specified using the training and development data. The experimental results has a state-of-the-art F-score comparable 2013 BioNLP shared task and gets 81 % precision in forming the gene regulation network. It shows we have a good performance in solving this problem.