A multi - objective evolutionary approach to reconstruct gene regulatory network using recurrent neural network model

With the advent of various data assaying techniques, gene expression time series data have become a useful resource to investigate the complex interactions occurring amongst the transcription factors and genes. While a number of methodologies have been de (GRN), the presence of high noise in gene expression data have made the estimation of non interactions among the genes an ill been proposed to efficiently reconstruct the skeletal structure of the biomolecular network using the Recurrent Neural Network (RNN) formalism. Moreover, this work presents a second criterion for model evaluation to exploit the sparse and scale free nature of GRN. T adapts the max-min in-degrees to effectively narrow down the search space, which reduces the computation time significantly and improves the model accuracy. The two well measures applied to the experimental studies on synthetic network with expression data having different noise-levels. The experimental results clearly demonstrate the suitability of the proposed method in capturing gene interactions correctly with high precision even with noisy time experiments carried out on analyzing well in Escherichia coli show a significant improvement in reconstructing the network of key regulatory genes.