Table of Contents
ISRN Bioinformatics
Volume 2012 (2012), Article ID 419419, 16 pages
Research Article

Hybrid-Controlled Neurofuzzy Networks Analysis Resulting in Genetic Regulatory Networks Reconstruction

1Electrical and Computer Engineering Department, Ryerson University, Toronto, ON, Canada M5B 2K3
2Electrical and Computer Engineering Department, K.N. Toosi University of Technology, Tehran 16315-1355, Iran
3Department of Chemical and Biological Engineering, Systems and Synthetic Biology Group, Chalmers University, 41296 Gutenberg, Sweden
4National Institute of Genetic Engineering and Biotechnology (NIGEB), Tehran 14965/161, Iran

Received 10 July 2012; Accepted 15 August 2012

Academic Editors: A. Bolshoy and C.-A. Tsai

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 reconstructing 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 on microarray expression profiles of S. cerevisiae cell cycle and demonstrate that the proposed algorithm not only selects the patterns of the time series gene expression data accurately, but also provides models with better reconstruction accuracy when compared with four published algorithms: DBNs, VBEM, time delay ARACNE, and PF subjected to LASSO. The accuracy of the proposed approach is evaluated in terms of recall and F-score for the network reconstruction task.