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     5,551 Downloads   9,419 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.

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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.


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