A review of modeling techniques for genetic regulatory networks
Understanding the genetic regulatory networks, the discovery of interactions between genes and understanding regulatory processes in a cell at the gene level are the major goals of system biology and computational biology. Modeling gene regulatory networks and describing the actions of the cells at...
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Wolters Kluwer Medknow Publications
2012-01-01
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Online Access: | http://www.jmss.mui.ac.ir/article.asp?issn=2228-7477;year=2012;volume=2;issue=1;spage=61;epage=70;aulast=Yaghoobi |
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doaj-e218302fe37746fd93534c14e6701a4c2020-11-24T21:46:40ZengWolters Kluwer Medknow PublicationsJournal of Medical Signals and Sensors2228-74772012-01-0121617010.4103/2228-7477.108179A review of modeling techniques for genetic regulatory networksHanif YaghoobiSiyamak HaghipourHossein HamzeiyMasoud Asadi-KhiaviUnderstanding the genetic regulatory networks, the discovery of interactions between genes and understanding regulatory processes in a cell at the gene level are the major goals of system biology and computational biology. Modeling gene regulatory networks and describing the actions of the cells at the molecular level are used in medicine and molecular biology applications such as metabolic pathways and drug discovery. Modeling these networks is also one of the important issues in genomic signal processing. After the advent of microarray technology, it is possible to model these networks using time-series data. In this paper, we provide an extensive review of methods that have been used on time-series data and represent the features, advantages and disadvantages of each. Also, we classify these methods according to their nature. A parallel study of these methods can lead to the discovery of new synthetic methods or improve previous methods.http://www.jmss.mui.ac.ir/article.asp?issn=2228-7477;year=2012;volume=2;issue=1;spage=61;epage=70;aulast=YaghoobiGene regulatory network (GRN)GRN reverse engineering modelsmicroarray time-series data |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Hanif Yaghoobi Siyamak Haghipour Hossein Hamzeiy Masoud Asadi-Khiavi |
spellingShingle |
Hanif Yaghoobi Siyamak Haghipour Hossein Hamzeiy Masoud Asadi-Khiavi A review of modeling techniques for genetic regulatory networks Journal of Medical Signals and Sensors Gene regulatory network (GRN) GRN reverse engineering models microarray time-series data |
author_facet |
Hanif Yaghoobi Siyamak Haghipour Hossein Hamzeiy Masoud Asadi-Khiavi |
author_sort |
Hanif Yaghoobi |
title |
A review of modeling techniques for genetic regulatory networks |
title_short |
A review of modeling techniques for genetic regulatory networks |
title_full |
A review of modeling techniques for genetic regulatory networks |
title_fullStr |
A review of modeling techniques for genetic regulatory networks |
title_full_unstemmed |
A review of modeling techniques for genetic regulatory networks |
title_sort |
review of modeling techniques for genetic regulatory networks |
publisher |
Wolters Kluwer Medknow Publications |
series |
Journal of Medical Signals and Sensors |
issn |
2228-7477 |
publishDate |
2012-01-01 |
description |
Understanding the genetic regulatory networks, the discovery of interactions between genes and understanding regulatory processes in a cell at the gene level are the major goals of system biology and computational biology. Modeling gene regulatory networks and describing the actions of the cells at the molecular level are used in medicine and molecular biology applications such as metabolic pathways and drug discovery. Modeling these networks is also one of the important issues in genomic signal processing. After the advent of microarray technology, it is possible to model these networks using time-series data. In this paper, we provide an extensive review of methods that have been used on time-series data and represent the features, advantages and disadvantages of each. Also, we classify these methods according to their nature. A parallel study of these methods can lead to the discovery of new synthetic methods or improve previous methods. |
topic |
Gene regulatory network (GRN) GRN reverse engineering models microarray time-series data |
url |
http://www.jmss.mui.ac.ir/article.asp?issn=2228-7477;year=2012;volume=2;issue=1;spage=61;epage=70;aulast=Yaghoobi |
work_keys_str_mv |
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