Improvement of reliabilities of regulations using a hierarchical structure in a genetic network

A number of genetic network inference methods have been proposed. These methods often infer many erroneous regulations. In order to decrease the number of erroneous regulations, this study uses a priori knowledge that biochemical networks exhibit hierarchical structures. This study detects the hierarchical structure in the target network using a hierarchical random graph model proposed by Clauset and colleagues. When the regulations inferred by the inference method are inconsistent with the detected hierarchical structure, we can conclude that they are unreasonable. However, it is not always easy to detect the hierarchical structure in the target network because of the regulations erroneously inferred by the inference method. In order to obtain a reasonable hierarchical structure, this study first infers a large number of genetic networks from the observed gene expression data by using a method that combines a genetic network inference method with a bootstrap method. We then extract a hierarchical structure from the inferred multiple genetic networks so that it is consistent with most of the networks. Through numerical experiments, we finally show that the use of the hierarchical structure in the network improves the reliabilities of regulations inferred by the genetic network inference method.