Journal of Biomedical Science and Engineering

Volume 3, Issue 6 (June 2010)

ISSN Print: 1937-6871   ISSN Online: 1937-688X

Google-based Impact Factor: 0.66  Citations  h5-index & Ranking

The application of hidden markov model in building genetic regulatory network

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DOI: 10.4236/jbise.2010.36086    5,608 Downloads   9,565 Views  Citations

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ABSTRACT

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.

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