JBiSE  Vol.3 No.2 , February 2010
Biclustering of time-lagged gene expression data using real number
Abstract: Analysis of gene expression data can help to find the time-lagged co-regulation of gene cluster. However, existing method just solve the problem under the condition when the data is discrete number. In this paper, we propose efficient algorithm to indentify time-lagged co-regulated gene cluster based on real number.
Cite this paper: nullLiu, F. and Wang, L. (2010) Biclustering of time-lagged gene expression data using real number. Journal of Biomedical Science and Engineering, 3, 217-220. doi: 10.4236/jbise.2010.32029.

[1]   Somogyi, R., Sniegoski, C.A. (1996) Modeling the complexity of genetic networks: Understanding muitigenic and pleiotropic regulation. Complexity, 1, 45-63.

[2]   Ji, L., Tan, K.-L. (2005) Identifying time-lagged gene clusters using gene expression data. Bioinformatics, 21(4), 509-516.

[3]   kato, M., Tsunoda, T., Takagi, T. (2001) Lag analysis of genetic networks in the cell cycle of budding yeast. Genome informatics, 12, 266-267.

[4]   Chen, T., Filkov, V., Skiena, S. (2001) Identifying gene regulatory networks from experimental data. In Proceeding of Third Annual International Conference on Research Computational Molecular Biology (Recomb’99). ACM press, 2001, 94-103.

[5]   Barash, Y., Friedman, N. (2002) Context-specific bayesian clustering for gene expression data. Journal of Computational Biology, 9, 169-191.

[6]   Kwon, A.T., Hoosand, H.H. (2003) Inference of transcriptional regulation relationships from gene expression data. Bioinformatics, 19, 905-912.

[7]   Yeung, L.K., Szeto, L.K., Liew, A.W., Yan, H. (2004) Dominant spectral component analysis for transcriptional regulations using microarray time-series data. Bioinformatics, 20(5), 742-749.

[8]   Cheng, Y., Church, G.M. (2000) Biclustering of gene expression data. Proceeding of Intelligent System for Molecule Biology (ISMB), 93-103.