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.
| Published in | Computational Biology and Bioinformatics (Volume 4, Issue 5) |
| DOI | 10.11648/j.cbb.20160405.11 |
| Page(s) | 37-44 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
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Copyright © The Author(s), 2024. Published by Science Publishing Group |
Gene Regulatory Network (GRN), GRN Inference, Swarm Intelligence, S-System Model
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APA Style
Md Julfikar Islam, M. S. R. Tanveer, M. A. H. Akhand. (2017). Gene Regulatory Network Inference Using Prominent Swarm Intelligence Methods. Computational Biology and Bioinformatics, 4(5), 37-44. https://doi.org/10.11648/j.cbb.20160405.11
ACS Style
Md Julfikar Islam; M. S. R. Tanveer; M. A. H. Akhand. Gene Regulatory Network Inference Using Prominent Swarm Intelligence Methods. Comput. Biol. Bioinform. 2017, 4(5), 37-44. doi: 10.11648/j.cbb.20160405.11
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Download@article{10.11648/j.cbb.20160405.11,
author = {Md Julfikar Islam and M. S. R. Tanveer and M. A. H. Akhand},
title = {Gene Regulatory Network Inference Using Prominent Swarm Intelligence Methods},
journal = {Computational Biology and Bioinformatics},
volume = {4},
number = {5},
pages = {37-44},
doi = {10.11648/j.cbb.20160405.11},
url = {https://doi.org/10.11648/j.cbb.20160405.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.cbb.20160405.11},
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.},
year = {2017}
}
TY - JOUR T1 - Gene Regulatory Network Inference Using Prominent Swarm Intelligence Methods AU - Md Julfikar Islam AU - M. S. R. Tanveer AU - M. A. H. Akhand Y1 - 2017/01/16 PY - 2017 N1 - https://doi.org/10.11648/j.cbb.20160405.11 DO - 10.11648/j.cbb.20160405.11 T2 - Computational Biology and Bioinformatics JF - Computational Biology and Bioinformatics JO - Computational Biology and Bioinformatics SP - 37 EP - 44 PB - Science Publishing Group SN - 2330-8281 UR - https://doi.org/10.11648/j.cbb.20160405.11 AB - 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. VL - 4 IS - 5 ER -
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