Inferring Genetic Networks with a Recurrent Neural Network Model Using Differential Evolution

In this chapter, we present an evolutionary approach for reverse-engineering gene regulatory networks (GRNs) from the temporal gene expression profile. The regulatory interaction among genes is modeled by the recurrent neural network (RNN) formalism. We used the differential evolution (DE) algorithm with a random restart strategy for inferring the underlying network structure as well as the regulatory parameters. The random restart mechanism is particularly useful for avoiding premature convergence and is hence expected to be valuable in recovering important regulations from noisy gene expression data. The algorithm has been applied for inferring regulation by analyzing gene expression data generated from both in silico and in vivo networks. We also investigate the effectiveness of the method in obtaining an acceptable network from a limited amount of noisy data.

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