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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 BN algorithm is approximately 70.8 and 7.6 times faster than the original BN algorithm when the error sizes of the Best-Fit Extension problem are 0 and 1, respectively. Further, the false positive error rate of the proposed CSTbased BN algorithm tends to be less than that of the original BN. Conclusion: The CST-based BN method dramatically improves the computation time of the original BN algorithm. Therefore, it can efficiently infer large-scale gene regulatory network mechanisms. Background The advancement of high-throughput technologies, such as DNA chips, has enabled the study of interactions and regulations among genes on a genome-wide scale. Recently, many algorithms have been introduced to determine gene regulatory networks based on such highthroughput microarray data, including linear models [1,2], Boolean networks [3-6], Bayesian networks [7,8], neural networks [9], and differential equations [1,10]. In the linear modeling of a genetic network, the expression data is fitted using a regression model, where the change in expression levels is a response for all other genes [1]. Although such standard linear modeling approaches enable the analysis of many different features of the modeled system, they are not effective in genomewide network discovery. This is because the number of candidate parameters and models is very high and therefore it is difficult to search efficiently and reliably with tight control on many false positives. Bayesian network algorithms have limitations with regard to the determination of an important network structure because of their complex modeling strategies (with a large number of parameters to be estimated) and a long computation time for searching all potential network strucPublished: 1 February 2007 BMC Bioinformatics 2007, 8:37 doi:10.1186/1471-2105-8-37 Received: 6 February 2006 Accepted: 1 February 2007 This article is available from: http://www.biomedcentral.com/1471-2105/8/37 © 2007 Kim et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.