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Advances in Bioinformatics
Volume 2009, Article ID 284251, 10 pages
Research Article

The KM-Algorithm Identifies Regulated Genes in Time Series Expression Data

1Department of Mathematics, San Jose State University, One Washington Square, San Jose, CA 95192, USA
2Department of Statistics, Purdue University, 150 N. University Street, West Lafayette, IN 47907, USA

Received 14 February 2009; Revised 12 June 2009; Accepted 9 July 2009

Academic Editor: Zhongming Zhao

Copyright © 2009 Martina Bremer and R. W. Doerge. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. J. K. Peeters and P. J. Van der Spek, “Growing applications and advancements in microarray technology and analysis tools,” Cell Biochemistry and Biophysics, vol. 43, no. 1, pp. 149–166, 2005. View at Publisher · View at Google Scholar
  2. A. L. Ghindilis, M. W. Smith, K. R. Schwarzkopf et al., “CombiMatrix oligonucleotide arrays: genotyping and gene expression assays employing electrochemical detection,” Biosensors and Bioelectronics, vol. 22, no. 9-10, pp. 1853–1860, 2007. View at Publisher · View at Google Scholar
  3. P. T. Spellman, G. Sherlock, M. Q. Zhang et al., “Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization,” Molecular Biology of the Cell, vol. 9, no. 12, pp. 3273–3297, 1998. View at Google Scholar
  4. Y. Fang, D. Choi, R. P. Searles, and W. D. Mathers, “A time course microarray study of gene expression in the mouse lacrimal gland after acute corneal trauma,” Investigative Ophthalmology & Visual Science, vol. 46, no. 2, pp. 461–469, 2005. View at Publisher · View at Google Scholar
  5. M. N. Arbeitman, E. E. M. Furlong, F. Imam et al., “Gene expression during the life cycle of Drosophila melanogaster,” Science, vol. 297, no. 5590, pp. 2270–2275, 2002. View at Publisher · View at Google Scholar
  6. X. Wen, S. Fuhrman, G. S. Michaels et al., “Large-scale temporal gene expression mapping of central nervous system development,” Proceedings of the National Academy of Sciences of the United States of America, vol. 95, no. 1, pp. 334–339, 1998. View at Publisher · View at Google Scholar
  7. R. Jenner, P. Kellam, X. Liu et al., “A framework for modelling short, high-dimensional multivariate time series: preliminary results in virus gene expression data analysis,” in Proceedings of the 4th International Conference on Intelligent Data Analysis, Springer, 2001.
  8. I. Yanai, J. O. Korbel, S. Boue, S. K. McWeeney, P. Bork, and M. J. Lercher, “Similar gene expression profiles do not imply similar tissue functions,” Trends in Genetics, vol. 22, no. 3, pp. 132–138, 2006. View at Publisher · View at Google Scholar
  9. T. Mestl, E. Plahte, and S. W. Omholt, “A mathematical framework for describing and analysing gene regulatory networks,” Journal of Theoretical Biology, vol. 176, no. 2, pp. 291–300, 1995. View at Publisher · View at Google Scholar
  10. I. Shmulevich, E. R. Dougherty, S. Kim, and W. Zhang, “Probabilistic Boolean networks: a rule-based uncertainty model for gene regulatory networks,” Bioinformatics, vol. 18, no. 2, pp. 261–274, 2002. View at Google Scholar
  11. P. J. Woolf and Y. Wang, “A fuzzy logic approach to analyzing gene expression data,” Physiological Genomics, vol. 3, no. 1, pp. 9–15, 2000. View at Google Scholar
  12. A. Fujita, J. R. Sato, H. M. Garay-Malpartida et al., “Modeling gene expression regulatory networks with the sparse vector autoregressive model,” BMC Systems Biology, vol. 1, article 39, 2007. View at Publisher · View at Google Scholar
  13. J. Schäfer and K. Strimmer, “An empirical Bayes approach to infering large-scale gene association networks,” Bioinformatics, vol. 21, no. 6, pp. 754–764, 2005. View at Google Scholar
  14. B.-E. Perrin, L. Ralaivola, A. Mazurie, S. Bottani, J. Mallet, and F. D'Alché-Buc, “Gene networks inference using dynamic Bayesian networks,” Bioinformatics, vol. 19, supplement 2, pp. ii138–ii148, 2003. View at Publisher · View at Google Scholar
  15. K. Murphy and S. Mian, “Modelling gene expression data using dynamic Bayesian networks,” Tech. Rep., Computer Science Division, University of California, Berkeley, Calif, USA, 1999. View at Google Scholar
  16. R. Yamaguchi and T. Higuchi, “State-space approach with the maximum likelihood principle to identify the system generating time-course gene expression data of yeast,” International Journal of Data Mining and Bioinformatics, vol. 1, no. 1, pp. 77–87, 2006. View at Publisher · View at Google Scholar
  17. R. Yamaguchi, R. Yoshida, S. Imoto, T. Higuchi, and S. Miyano, “Finding module-based gene networks with state-space models—mining high-dimensional and short time-course gene expression data,” IEEE Signal Processing Magazine, vol. 24, no. 1, pp. 37–46, 2007. View at Google Scholar
  18. B. Di Camillo, G. Toffolo, S. K. Nair, L. J. Greenlund, and C. Cobelli, “Significance analysis of microarray transcript levels in time series experiments,” BMC Bioinformatics, vol. 8, supplement 1, 2007. View at Publisher · View at Google Scholar
  19. J. L. DeRisi, V. R. Iyer, and P. O. Brown, “Exploring the metabolic and genetic control of gene expression on a genomic scale,” Science, vol. 278, no. 5338, pp. 680–686, 1997. View at Publisher · View at Google Scholar
  20. T. Chen, V. Filkov, and S. Skiena, “Identifying gene regulatory networks from experimenta data,” in Proceedings of the 3rd Annual International Conference on Computational Molecular Biology (RECOMB '99), April 1999.
  21. R. E. Kalman, “A new approach to linear filtering and prediction problems,” Journal of Basic Engineering, vol. 82, pp. 35–45, 1960. View at Google Scholar
  22. A. P. Dempster, N. M. Laird, and D. B. Rubin, “Maximum likelihood from incomplete data via the EM algorithm,” Journal of the Royal Statistical Society B, vol. 39, pp. 1–38, 1977. View at Google Scholar
  23. J. Chandrasekar, I. S. Kim, D. S. Bernstein, and A. J. Ridley, “Cholesky-based reduced-rank square-root Kalman filtering,” in Proceedings of the American Control Conference, 2008.
  24. G. Schwarz, “Estimating the dimension of a model,” The Annals of Statistics, vol. 6, no. 2, pp. 461–464, 1978. View at Google Scholar
  25. H. Akaike, “Fitting autoregressive models for prediction,” Annals of the Institute of Statistical Mathematics, vol. 21, pp. 243–247, 1969. View at Google Scholar
  26. M. Aoki and A. Havenner, “State space modeling of multiple time series,” Econometric Review, vol. 10, pp. 1–59, 1991. View at Google Scholar
  27. R. B. Lehoucq, D. C. Sorensen, and C. Yang, ARPACK Users' Guide: Solution of Large-Scale Eigenvalue Problems with Implicitly Restarted Arnoldi Methods, SIAM, Philadelphia, Pa, USA, 1998.
  28. I. Icaza and R. H. Jones, “A state-space EM algorithm for longitudinal data,” Journal of Time Series Analysis, vol. 20, no. 5, pp. 537–550, 1999. View at Google Scholar
  29. M. M. Bremer, Identifying regulated genes through the correlation structure of time-dependent microarray data, Ph.D. thesis, Purdue University, West Lafayette, Ind, USA, 2006.
  30. G. J. Steel, D. M. Fullerton, J. R. Tyson, and C. J. Stirling, “Coordinated activation of Hsp70 chaperones,” Science, vol. 303, no. 5654, pp. 98–101, 2004. View at Publisher · View at Google Scholar
  31. W.-K. Huh, J. V. Falvo, L. C. Gerke et al., “Global analysis of protein localization in budding yeast,” Nature, vol. 425, no. 6959, pp. 686–691, 2003. View at Publisher · View at Google Scholar
  32. A. S. Ford, Q. Guan, E. Neeno-Eckwall, and M. R. Culbertson, “Ebs1p, a negative regulator of gene expression controlled by the Upf proteins in the yeast Saccharomyces cerevisiae,” Eukaryotic Cell, vol. 5, no. 2, pp. 301–312, 2006. View at Publisher · View at Google Scholar
  33. P. Dagum and M. Luby, “Approximating probabilistic inference in Bayesian belief networks is np-hard,” Artificial Intelligence, vol. 60, no. 1, pp. 141–153, 1993. View at Google Scholar
  34. S. Schuster, D. A. Fell, and T. Dandekar, “A general definition of metabolic pathways useful for systematic organization and analysis of complex metabolic networks,” Nature Biotechnology, vol. 18, no. 3, pp. 326–332, 2000. View at Publisher · View at Google Scholar