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      Reconstructing gene regulatory networks with bayesian networks by combining expression data with multiple sources of prior knowledge.

      Statistical applications in genetics and molecular biology
      Probability, Gene Expression, Immunoprecipitation, Bayes Theorem, Markov Chains, Monte Carlo Method, Genes, Regulator

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          Abstract

          There have been various attempts to reconstruct gene regulatory networks from microarray expression data in the past. However, owing to the limited amount of independent experimental conditions and noise inherent in the measurements, the results have been rather modest so far. For this reason it seems advisable to include biological prior knowledge, related, for instance, to transcription factor binding locations in promoter regions or partially known signalling pathways from the literature. In the present paper, we consider a Bayesian approach to systematically integrate expression data with multiple sources of prior knowledge. Each source is encoded via a separate energy function, from which a prior distribution over network structures in the form of a Gibbs distribution is constructed. The hyperparameters associated with the different sources of prior knowledge, which measure the influence of the respective prior relative to the data, are sampled from the posterior distribution with MCMC. We have evaluated the proposed scheme on the yeast cell cycle and the Raf signalling pathway. Our findings quantify to what extent the inclusion of independent prior knowledge improves the network reconstruction accuracy, and the values of the hyperparameters inferred with the proposed scheme were found to be close to optimal with respect to minimizing the reconstruction error.

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          Author and article information

          Journal
          10.2202/1544-6115.1282
          17542777

          Chemistry
          Probability,Gene Expression,Immunoprecipitation,Bayes Theorem,Markov Chains,Monte Carlo Method,Genes, Regulator

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