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The Scientific World Journal
Volume 2015, Article ID 148010, 11 pages
Review Article

State of the Art of Fuzzy Methods for Gene Regulatory Networks Inference

1Information Technology Department, Computer College, Qassim University, Buraydah 51452, Saudi Arabia
2Computer Science Department, Computer College, Qassim University, Buraydah 51452, Saudi Arabia

Received 27 July 2014; Accepted 3 December 2014

Academic Editor: Albert Victoire

Copyright © 2015 Tuqyah Abdullah Al Qazlan 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.


To address one of the most challenging issues at the cellular level, this paper surveys the fuzzy methods used in gene regulatory networks (GRNs) inference. GRNs represent causal relationships between genes that have a direct influence, trough protein production, on the life and the development of living organisms and provide a useful contribution to the understanding of the cellular functions as well as the mechanisms of diseases. Fuzzy systems are based on handling imprecise knowledge, such as biological information. They provide viable computational tools for inferring GRNs from gene expression data, thus contributing to the discovery of gene interactions responsible for specific diseases and/or ad hoc correcting therapies. Increasing computational power and high throughput technologies have provided powerful means to manage these challenging digital ecosystems at different levels from cell to society globally. The main aim of this paper is to report, present, and discuss the main contributions of this multidisciplinary field in a coherent and structured framework.