Neural network based gene regulatory network reconstruction

Abstract

Gene Regulatory Networks (GRN) is used to model the regulations in living organisms. Inferring genetic network from different experimental high throughput biological data (like microarray) is a challenging job for all researchers. In this paper, Artificial Neural Network, which is a very effective soft computing tool to learn and model the dynamics or dependencies between genes, is used for reconstruction of small scale GRN from the reduced microarray dataset of Lung Adenocarcinoma. The significances of regulations of one gene to other genes of the system are expressed by a weight matrix which is computed using Perceptron based biologically significant weight updating method by minimizing the error during learning. Based on the values of elements of filtered weight matrix, a directed weighted graph can be drawn successfully that denotes gene regulatory network.