Abstract
It is important to detect interaction effect of multiple genes during certain biological process. In this paper, we proposed, from systems biology perspective, the concept of co-regulated gene module, which consists of genes that are regulated by the same regulator(s). Given a time series gene expression data, a hidden Markov modelbased Bayesian model was developed to calculate the likelihood of the observed data, assuming the co-regulated gene modules are known. We further developed a Gibbs sampling strategy that is integrated with reversible jump Markov chain Monte Carlo to obtain the posterior probabilities of the co-regulated gene modules. Simulation study validated the proposed method. When compared with two existing methods, the proposed approach significantly outperformed the conventional methods.
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Tang, W., Li, R., Li, S. et al. Co-regulated gene module detection for time series gene expression data. Front. Electr. Electron. Eng. 7, 357–366 (2012). https://doi.org/10.1007/s11460-012-0207-x
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DOI: https://doi.org/10.1007/s11460-012-0207-x