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Extracting Gene Regulation Information from Microarray Time-Series Data Using Hidden Markov Models

  • Osman N. Yoğurtçu
  • Engin Erzin
  • Attila Gürsoy
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4263)

Abstract

Finding gene regulation information from microarray time-series data is important to uncover transcriptional regulatory networks. Pearson correlation is the widely used method to find similarity between time-series data. However, correlation approach fails to identify gene regulations if time-series expressions do not have global similarity, which is mostly the case. Assuming that gene regulation time-series data exhibits temporal patterns other than global similarities, one can model these temporal patterns. Hidden Markov models (HMMs) are well established structures to learn and model temporal patterns. In this study, we propose a new method to identify regulation relationships from microarray time-series data using HMMs.

We showed that the proposed HMM based approach detects gene regulations, which are not captured by correlation methods. We also compared our method with recently proposed gene regulation detection approaches including edge detection, event method and dominant spectral component analysis. Results on Spellman’s α-synchronized yeast cell-cycle data clearly present that HMM approach is superior to previous methods.

Keywords

Hide Markov Model Gene Pair Unknown Regulation Class Conditional Probability Gaussian Mixture Density 
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.

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References

  1. 1.
    Eisen, M., Spellman, P., Brown, P., Bolstein, D.: Cluster analysis and display of genome-wide expression patterns. Proc. Natl. Acad. Sci. USA 95, 14863–14868 (1998)CrossRefGoogle Scholar
  2. 2.
    Filkov, V., Skiena, S., Zhi, J.: Analysis techniques for microarray time-series data. J. Comput. Biol. 9, 317–330 (2002)CrossRefGoogle Scholar
  3. 3.
    Kwon, A., Hoos, H., Ng, R.: Inference of transcriptional regulation relationships from gene expression data. Bioinformatics 19, 905–912 (2003)CrossRefGoogle Scholar
  4. 4.
    Friedman, N., Linial, M., Nachman, I., Pr, D.: Using bayesian networks to analyze expression data. J. Comput. Biol. 7, 601–620 (2000)CrossRefGoogle Scholar
  5. 5.
    De Hoon, M., Imoto, S., Miyano, S.: Inferring gene regulatory networks from time-ordered gene expression data using differential equations. In: Lange, S., Satoh, K., Smith, C.H. (eds.) DS 2002. LNCS, vol. 2534, pp. 267–274. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  6. 6.
    Yeung, L.K.e.: Measuring correlation between microarray time-series data using dominant spectral component. Bioinformatics 20(5), 742–749 (2004)CrossRefGoogle Scholar
  7. 7.
    Churchill, G.: Stochastic models for heterogeneous DNA sequences. B. Math. Biol. 51, 79–94 (1989)MATHMathSciNetGoogle Scholar
  8. 8.
    Cardon, L., Stormo, G.: Expectation maximization algorithm for identifying protein-binding sites with variable lengths from unaligned dna fragments. Journal of Molecular Biology 223, 159–170 (1992)CrossRefGoogle Scholar
  9. 9.
    Krogh, A., Brown, M., Mian, S., Sjolander, K., Haussler, D.: Hidden markov models in computational biology: Applications to protein modeling. Journal of Molecular Biology 235, 1501–1531 (1994)CrossRefGoogle Scholar
  10. 10.
    Spellman, P., Sherlock, G., Zhang, M., Iyer, V., Anders, K., Eisen, M., Brown, P., 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)Google Scholar
  11. 11.
    Baum, L.: An inequality and associated maximization technique in statistical estimation of probabilistic functions of a Markov process. Inequalities 3, 1–8 (1972)Google Scholar
  12. 12.
    Young, S., Evermann, G., Hain, T., Kershaw, D., Moore, G., Odell, J., Ollason, D., Povey, D., Valtchev, V., Woodland, P.: The HTK Book (for HTK version 3.2.1). Cambridge University Engineering Department, Cambridge (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Osman N. Yoğurtçu
    • 1
  • Engin Erzin
    • 1
  • Attila Gürsoy
    • 1
  1. 1.Computer EngineeringKoç UniversityTurkey

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