Abstract
Boolean network is one of the commonly used methods for building gene regulatory networks from time series microarray data. However, it has a major drawback that requires heavy computing times to infer large scale gene networks. This paper proposes a variable selection method to reduce Boolean network computing times using the chi-square statistics for testing independence in two way contingency tables. We compare the computing times and the accuracy of the estimated network structure by the proposed method with those of the original Boolean network method. For the comparative studies, we use simulated data and a real yeast cell-cycle gene expression data (Spellman et al., 1998). The comparative results show that the proposed variable selection method improves the computing time of Boolean network algorithm. We expect the proposed variable selection method to be more efficient for the large scale gene regulatory network studies.
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Kim, H., Lee, J.K., Park, T. (2007). Improvement of Computing Times in Boolean Networks Using Chi-square Tests. In: Eskin, E., Ideker, T., Raphael, B., Workman, C. (eds) Systems Biology and Regulatory Genomics. RSB RRG 2005 2005. Lecture Notes in Computer Science(), vol 4023. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-48540-7_7
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DOI: https://doi.org/10.1007/978-3-540-48540-7_7
Publisher Name: Springer, Berlin, Heidelberg
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