Gene Regulatory Network Inference Using Prominent Swarm Intelligence Methods
Computational Biology and Bioinformatics
Volume 4, Issue 5, October 2016, Pages: 37-44
Received: Nov. 25, 2016; Accepted: Dec. 12, 2016; Published: Jan. 16, 2017
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Authors
Md Julfikar Islam, Dept. of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna, Bangladesh
M. S. R. Tanveer, Dept. of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna, Bangladesh
M. A. H. Akhand, Dept. of Computer Science and Engineering, Khulna University of Engineering & Technology, Khulna, Bangladesh
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Abstract
Genes are the basic blue print of life in an organism containing the physiological and behavioral characteristics. A gene regulatory network (GRN) is a set of genes, or parts of genes, that interact with each other to control a specific cell function. GRN inference is the reverse engineering approach to predict the biological network from the gene expression data. Biochemical system theory based S-System is a popular model in GRN inference and the model is defined with its different parameters. The task of S-System based GRN inference is its parameter estimation which is an optimization problem. Several studies employed Particle Swarm Optimization (PSO) and other pioneer optimization techniques to estimate S-System model. In this paper several prominent swarm intelligence (SI) techniques have been studied and adapted for S-System parameter estimation. They are Group Search Optimizer, Grey Wolf Optimizer and PSO. Proficiency of optimization techniques are compared to infer GRN from SOS DNA real gene expression data and DREAM 4 Silico data.
Keywords
Gene Regulatory Network (GRN), GRN Inference, Swarm Intelligence, S-System Model
To cite this article
Md Julfikar Islam, M. S. R. Tanveer, M. A. H. Akhand, Gene Regulatory Network Inference Using Prominent Swarm Intelligence Methods, Computational Biology and Bioinformatics. Vol. 4, No. 5, 2016, pp. 37-44. doi: 10.11648/j.cbb.20160405.11
Copyright
Copyright © 2016 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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