doi:10.5402/2012/419419 Research Article Hybrid-Controlled Neurofuzzy Networks Analysis Resulting in Genetic Regulatory Networks Reconstruction

Copyright © 2012 Roozbeh Manshaei et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Reverse engineering of gene regulatory networks (GRNs) is the process of estimating genetic interactions of a cellular system from gene expression data. In this paper, we propose a novel hybrid systematic algorithm based on neurofuzzy network for recon-structing GRNs from observational gene expression data when only a medium-small number of measurements are available. The approach uses fuzzy logic to transform gene expression values into qualitative descriptors that can be evaluated by using a set of defined rules. The algorithm uses neurofuzzy network to model genes effects on other genes followed by four stages of decision making to extract gene interactions. One of the main features of the proposed algorithm is that an optimal number of fuzzy rules can be easily and rapidly extracted without overparameterizing. Data analysis and simulation are conducted onmicroarray expres-sion profiles of S. cerevisiae cell cycle and demonstrate that the proposed algorithm not only selects the patterns of the time serie