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http://dx.doi.org/10.3745/KIPSTD.2007.14-D.1.009

Constructing Gene Regulatory Networks using Frequent Gene Expression Pattern and Chain Rules  

Lee, Heon-Gyu (충북대학교 대학원 전자계산학과)
Ryu, Keun-Ho (충북대학교 전기전자 컴퓨터공학부)
Joung, Doo-Young (충북대학교 전기전자 컴퓨터공학부)
Abstract
Groups of genes control the functioning of a cell by complex interactions. Such interactions of gene groups are tailed Gene Regulatory Networks(GRNs). Two previous data mining approaches, clustering and classification, have been used to analyze gene expression data. Though these mining tools are useful for determining membership of genes by homology, they don't identify the regulatory relationships among genes found in the same class of molecular actions. Furthermore, we need to understand the mechanism of how genes relate and how they regulate one another. In order to detect regulatory relationships among genes from time-series Microarray data, we propose a novel approach using frequent pattern mining and chain rules. In this approach, we propose a method for transforming gene expression data to make suitable for frequent pattern mining, and gene expression patterns we detected by applying the FP-growth algorithm. Next, we construct a gene regulatory network from frequent gene patterns using chain rules. Finally, we validate our proposed method through our experimental results, which are consistent with published results.
Keywords
Gene Regulatory Network; Frequent Pattern Mining; Chain Rules; Gene Interaction;
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1 Elledge, S. J. and Davis, R. W., 'Identification of the DNA damage-responsive element of RNR2 and evidence that four distinct cellular factors bind it', Molecular and Cell Biology, 9(12):5373-86. 1989   DOI
2 Friedman, N., Linial, M., Nachman, I. and Pe'er, D., 'Using Bayesian networks to analyze expression data', Journal of Computational Biology, 7:601-620, 2000   DOI   ScienceOn
3 Akutsu, T., Miyano, S., and kuhara, S., 'Identification of genetic networks from a small number of gene expression patterns under the Boolean network model', Pacific Symposium on Biocomputing 17-28, 1999
4 Han, J., Pei, J., Yin, Y., 'Mining frequent patterns without candidate generation'. In SIGMOD'00, Dallas, TX, 2000   DOI
5 Brown, M. P., Grundy, W. N., Lin, D., Sugnet, C. W., Furey, T. S., Ares Jr., and Haussler, D., 'Knowledge-based analysis of microarray gene expression data by using support vector machines'. PNAS, 4;97(1):262-7. 2000
6 Van Someren, E. P., Wessels, L. F. A., and Reinders, 'Linear modeling of genetic networks from experimental data. Proc., ISMB, 355-366, 2000
7 Li, W., Han, J. and Pei, J., 'CMAR: Accurate and Efficient Classification Based on Multiple Association Rules', Proc., Interna'l Conf, on Data Mining, 2001   DOI
8 Husmeier, D., 'Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networks', Bioinformatics, 19: 2271-2282, 2003   DOI   ScienceOn
9 Ting Chen, Vladimir Filkov, Steven S. Skiena, 'Identifying Gene Regulatory Networks from Experimental Data', RECOMB, 94-103, 1999
10 Tamayo, P., Slonim, D., Mesirov, J., Zhu, Q., Kitareewan, S., Dmitrovsky, E., Lander, E. and Golub, T. 'Interpreting patterns of gene expression with selforganizing maps'. PNAS, 96:2907-2912. 1999   DOI
11 Forsyth, R. and Rada, R., 'Machine Learning applications in Expert Systems and Information Retrieval', Ellis Horwood Limited, 1986
12 Meretakis, D. and Wuthrich, B., 'Extending naive bayes classifiers using long itemsets', Proc., the 5th ACM SIGKDD Conference on Knowledge Discovery and Data Min-ing, 165-174, 1999   DOI
13 Yeast Protein Database (YPD) (http://www.proteome.com)
14 Spellman, P. T., Sherlock, G., Zhang, M. Q., Iyer, V. R., Anders, K., Eisen, M. B., Brown, P.O., Botstein, D. and Futcher, B., 'Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization', Molecular Biology of the Cell, 9:3273-3297. 1998   DOI
15 Holter, N. S., Maritan, A., Fedoroff, N. V. and Banavar, J. R., 'Dynamic modeling of gene expression data, Proc., Natl, Acad. Sci. 1693-1698, 2000
16 Rishi Khan, Yujing Zeng, Javier GarciaFrias and Guang Gao, 'A Bayesian Modeling Framework for Genetic Regulation', Proc., CSB'02, 2002   DOI
17 Eisen, M. B., Spellman, P. T., Brown, P.O., and Botstein, D., 'Cluster Analysis and Display of Genome-Wide Expression Patterns'. Proc., National Academy of Science. 95: 14863-14868, 1998   DOI