%0 Journal Article
%T Hybrid-Controlled Neurofuzzy Networks Analysis Resulting in Genetic Regulatory Networks Reconstruction
%A Roozbeh Manshaei
%A Pooya Sobhe Bidari
%A Mahdi Aliyari Shoorehdeli
%A Amir Feizi
%A Tahmineh Lohrasebi
%A Mohammad Ali Malboobi
%A Matthew Kyan
%A Javad Alirezaie
%J ISRN Bioinformatics
%D 2012
%R 10.5402/2012/419419
%X 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. 1. Introduction Biological systems are inherently stochastic, uncertain, and fuzzy [1]. Therefore, research in bioinformatics and computational biology, where computer technologies are applied to manage and analyze biological data and make computational models, is faced with a great deal of uncertainty. For instance, growth and development as well as environmental stresses can all contribute to change in gene expression levels. In addition, under such conditions, some genes influence the expression of other genes and their functionalities. With the advent of high-throughput technologies in transcriptomics, proteomics, and metabolomics, now, biologists have the ability to investigate the expression of genes and consequences on a genome-wide scale. Gene expression data in the form of high-throughput microarray experiments measure the amounts of RNA associated with each of thousands of genes in parallel. Time-series microarrays have attracted biologists¡¯ interests for deciphering the dynamic and complex nature of biological networks. Time-series microarrays record multiple expression profiles at discrete time points (i.e., hours or days) of a continuous cellular process. Thus, analytical methods are needed to handle many genes with uncertain
%U http://www.hindawi.com/journals/isrn.bioinformatics/2012/419419/