Boolean networks using the chi-square test for inferring large-scale gene regulatory networks

Background: Boolean network (BN) modeling is a commonly used method for constructing gene regulatory networks from time series microarray data. However, its major drawback is that its computation time is very high or often impractical to construct large-scale gene networks. We propose a variable selection method that are not only reduces BN computation times significantly but also obtains optimal network constructions by using chi-square statistics for testing the independence in contingency tables. Results: Both the computation time and accuracy of the network structures estimated by the proposed method are compared with those of the original BN methods on simulated and real yeast cell cycle microarray gene expression data sets. Our results reveal that the proposed chi-square testing (CST)-based BN method significantly improves the computation time, while its ability to identify all the true network mechanisms was effectively the same as that of full-search BN methods. The proposed B...

• Type: Journal
• Year: 2007
• Where: BMCBI
• Authors: Haseong Kim, Jae K. Lee, Taesung Park
• Keywords: BMCBI 2007, Computation Time, Gene Regulatory Networks, Original Bn