Construction of genetic network using evolutionary algorithm and combined fitness function.
This paper proposes a method to capture the dynamics in gene expression data using S-system formalism and construct genetic network models. Our purposed method exploits the probabilistic heuristic search and divide-and-conquer approach to estimate the network structure. In evaluating the network structure, we attempt a primitive integration of other knowledge to the statistical criterion. The Z-score is used to analyze the robust and significant parameters from stochastic search results. We evaluated the proposed method on artificially generated data and E.coli mRNA expression data.
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