The application of hidden markov model in building genetic regulatory network
Rui-Rui Ji, Ding Liu, Wen Zhang
DOI: 10.4236/jbise.2010.36086   PDF   HTML     5,065 Downloads   8,507 Views   Citations


The research hotspot in post-genomic era is from sequence to function. Building genetic regulatory network (GRN) can help to understand the regulatory mechanism between genes and the function of organisms. Probabilistic GRN has been paid more attention recently. This paper discusses the Hidden Markov Model (HMM) approach served as a tool to build GRN. Different genes with similar expression levels are considered as different states during training HMM. The probable regulatory genes of target genes can be found out through the resulting states transition matrix and the determinate regulatory functions can be predicted using nonlinear regression algorithm. The experiments on artificial and real-life datasets show the effectiveness of HMM in building GRN.

Share and Cite:

Ji, R. , Liu, D. and Zhang, W. (2010) The application of hidden markov model in building genetic regulatory network. Journal of Biomedical Science and Engineering, 3, 633-637. doi: 10.4236/jbise.2010.36086.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Zhao, G.P. (2002) Bioinformatics. Science Press, Beijing.
[2] Hidde, D.J. (2002) Modeling and simulation of genetic regulatory systems: A literature review. Journal of Computational Biology, 9(9), 67-103.
[3] Akustu, T., Miyano, S. and Kuhara, S. (2000) Inferring Qualitative relations in genetic networks and metabolic arrays. Bioinformatics, 16(8), 727-734.
[4] Bower, J. (2001) Computational modeling of genetic and biochemical networks. MIT Press, Cambridge.
[5] Hartemink, A., Gifford, D., Jaakkola, T., et al. (2002) Bayesian methods for elucidating genetic regulatory networks. IEEE Intelligent Systems, 17(2), 37-43.
[6] Ching, W., Fung, E., Ng, M. and Akustu, T. (2005) On construction of stochastic genetic networks based on gene expression sequences. International Journal of Neural Systems, 15(4), 297-310.
[7] Zhang, S.-Q., Ching, W.-K. and Yue, J. (2008) Construction and control og genetic regulatory networks: A multivariate Markov chain approach. Journal of Biomedical Science and Engineering, 1, 15-21.
[8] Tsamardinos, I., Brown, L.E. and Aliferis, C.F. (2006) The max-min hill-climbing bayesian network structure learning algorithm. Machine Learning, 65(1), 31-78.
[9] Spellman, P.T., Sherlock, G., Zhang, M.Q., Iyer, V.R., Amders, K., Eisen, M.B., Brown, P.O., Botstein, D. and Futcher, B. (1997) Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Molecular Biology of the Cell, 9(12), 3273-3297.
[10] Futcher B. (2002) Transcriptional regulatory networks and yeast cell cycle. Current Opinion in Cell Biology, 14(6), 676-683.
[11] Paul A.F. M.D. (2004) IncyteDB/OL. http://www.i-ncyte. com/proteome/YPD
[12] Zhang, Z.-F. (2004) Constructing and predicting gene regulatory network using micro-array data. National Central University, Taiwan.

Copyright © 2020 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.