A recurrent fuzzy neural model of a gene regulatory network for knowledge extraction using differential evolution

A gene regulatory network describes the influence of genes over others. This paper attempts to model gene regulatory network by a recurrent neural net with fuzzy membership distribution of weights. A cost function is designed to match the response of neurons in the network with the gene expression data, and a differential evolution algorithm is used to minimize the cost function. The minimization yields fuzzy membership distribution of weights, which on de-fuzzification provides the desired signed weights of the gene regulatory network. Computer simulation reveals that the proposed method outperforms existing techniques in detecting sign, and magnitude of weights of the regulatory network.

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