Feature Reduction for Gene Regulatory Network Control

Scalability is one of the most important issues in control problems, including the control of gene regulatory networks. In this paper, we argue that it is possible to improve scalability of gene regulatory networks control by reducing the number of genes to be considered by the control policy; and consequently propose a novel method to estimate genes that are less important for control. The reported test results on real and synthetic data demonstrate the applicability and effectiveness of the proposed approach.