Reverse engineering gene regulatory network from microarray data using linear time-variant model

nd: Gene regulatory network is an abstract mapping of gene regulations in living cells that can help to predict the system behavior of living organisms. Such prediction capability can potentially lead to the development of improved diagnostic tests and therapeutics. DNA microarrays, which measure the expression level of thousands of genes in parallel, constitute the numeric seed for the inference of gene regulatory networks. In this paper, we have proposed a new approach for inferring gene regulatory networks from time-series gene expression data using linear time-variant model. Here, Self-Adaptive Differential Evolution, a versatile and robust Evolutionary Algorithm, is used as the learning paradigm. Results: To assess the potency of the proposed work, a well known nonlinear synthetic network has been used. The reconstruction method has inferred this synthetic network topology and the associated regulatory parameters with high accuracy from both the noise-free and noisy time-series d...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Mitra Kabir, Nasimul Noman, Hitoshi Iba
• Keywords: BMCBI 2010, Expression Data, Gene Regulatory Networks, Time-series Gene Expression