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.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Agresti, A.: Categorical data analysis, 2nd edn. Wiley-interscience, Chichester (2002)
Akutsu, T., Miyano, S., Kuhara, S.: Identification of genetic networks from a small number of gene expression patterns under the Boolean network model. Pacific Symposium on Biocomputing 4, 17–28 (1999)
Akutsu, T., Miyano, S.: Selecting informative genes for cancer classification using gene expression data. In: Proceddings of the IEEE-EURASIP Workshop on NonlinSignal and Image Processing (NSIP), Baltimore, MD, pp. 3–6. IEEE Computer Society Press, Los Alamitos (2001)
Boros, E., Hammer, P.L., Ibaraki, T., Kogan, A.: Logical analysis of numerical data. Math. Program 79, 163–190 (1997)
Boros, E., Ibaraki, T., Makino, K.: Error-Free and Best-Fit Extensions of partially defined Boolean functions. Information and Computation 140, 254–283 (1998)
Dougherty, J., Kohavi, R., Sahami, M.: Supervised and unsupervised discretization of continuous features. In: Proceedings of the Twelfth International Conference on Machine Learning, Tahoe City, CA, pp. 194–202. Morgan Kaufmann, San Francisco (1995)
Huang, S.: Gene expression profiling, genetic networks and cellular states: An integrating concept for tumorigenesis and drug discovery. Journal of Molecular Medicine 77, 469–480 (1999)
Johnson, S.: Boolean network inference and experiment design for the B-Cell single ligand screen. AfCS annual meeting (2004)
Kauffman, S., Peterson, C., Samuelsson, B., Troein, C.: Random boolean network models and the yeast transcriptional network. Proc. Natl Acad. Sci. USA 100, 14796–14799 (2003)
Kauffman, S.A.: Metabolic stability and epigenesis in randomly constructed genetic nets. Journal of Theoretical Biology 22, 437–467 (1969)
Kauffman, S.A.: The Origins of Order: Self-organization and Selection in Evolution. Oxford University Press, New York (1993)
Lähdesmaki, H., Shmulevich, I., Yli-Harja, O.: On learning gene regulatory networks under the Boolean network model. Machine Learning 52, 147–167 (2003)
Liang, S., Fuhrman, S., Somogyi, R.: REVEAL, A general reverse engineering algorithm for inference of genetic network architectures. Pacific Symposium on Biocomputing 3, 18–29 (1998)
Pfahringer, B.: Compression-based discretization of continuous attributes. In: Prieditis, A., Russell, S. (eds.) Machine Learning: Procees of the Twelfth International Conference, Morgan Kaufmann, San Francisco (1995)
Schilstra, M.J., Bolouri, H.: Modeling the regulation of gene expression in genetic regulatory networks (2003), http://strc.herts.ac.uk/bio/maria/NetBuilder/
Schwarzer, C.: Matlab Random Boolean Network Toolbox (2003), http://www.teuscher.ch/rbntoolbox/index.html
Shmulevich, I., Dougherty, E.R., Seungchan, K., Zhang, W.: Probabilistic Boolean networks: A rule-based uncertainty model for gene regulatory networks. Bioinformatics 18, 261–274 (2002a)
Shmulevich, I., Saarinen, A., Yli-Harja, O., Astola, J.: Inference of genetic regulatory networks under the Best-Fit Exension paradigm. In: Zhang, W., Shmulevich, I. (eds.) Computational And Statistical Approaches To Genomics, Kluwer Academic Publishers, Boston (2002b)
Shmulevich, I., Zang, W.: Binary analysis and optimization-based normalization of gene expression data. Bioinformatics 18(4), 555–565 (2002)
Spellman, P.T., Sherlock, G., Zhang, M.Q., Iyer, V.R., Anders, K., Eisen, M.B., Brown, P.O., Botstein, D., Futcher, B.: Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Mol. Biol. Cell. 9, 3273–3297 (1998)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-540-48540-7_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-48293-2
Online ISBN: 978-3-540-48540-7
eBook Packages: Computer ScienceComputer Science (R0)