Research trends in microarray data analysis: modelling gene regulatory network by integrating transcription factors data / Farzana Kabir Ahmad and Siti Sakira Kamaruddin

The invention of microarray technology has enabled expression levels of thousands of genes to be monitored at once. This modernized approach has created large amount of data to be examined. Recently, gene regulatory network has been an interesting topic and generated impressive research goals in co...

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Main Authors:	Kabir Ahmad, Farzana, Kamaruddin, Siti Sakira
Format:	Article
Language:	English
Published:	Institute of Research Management and Innovation (IRMI) 2015
Subjects:	Bayesian statistical decision theory Gene Regulatory Networks Transcription factors
Online Access:	http://ir.uitm.edu.my/id/eprint/13580/ http://ir.uitm.edu.my/id/eprint/13580/1/AJ_FARZANA%20KABIR%20AHMAD%20SRJ%2015.pdf

id	uitm-13580
recordtype	eprints
spelling	uitm-135802016-05-30T04:57:11Z http://ir.uitm.edu.my/id/eprint/13580/ Research trends in microarray data analysis: modelling gene regulatory network by integrating transcription factors data / Farzana Kabir Ahmad and Siti Sakira Kamaruddin Kabir Ahmad, Farzana Kamaruddin, Siti Sakira Bayesian statistical decision theory Gene Regulatory Networks Transcription factors The invention of microarray technology has enabled expression levels of thousands of genes to be monitored at once. This modernized approach has created large amount of data to be examined. Recently, gene regulatory network has been an interesting topic and generated impressive research goals in computational biology. Better understanding of the genetic regulatory processes would bring significant implications in the biomedical fields and many other pharmaceutical industries. As a result, various mathematical and computational methods have been used to model gene regulatory network from microarray data. Amongst those methods, the Bayesian network model attracts the most attention and has become the prominent technique since it can capture nonlinear and stochastic relationships between variables. However, structure learning of this model is NP-hard and computationally complex as the number of potential edges increase drastically with the number of genes. In addition, most of the studies only focused on the predicted results while neglecting the fact that microarray data is a fragmented information on the whole biological process. Hence, this study proposed a network-based inference model that combined biological knowledge in order to verify the constructed gene regulatory relationships. The gene regulatory network is constructed using Bayesian network based on low-order conditional independence approach. This technique aims to identify from the data the dependencies to construct the network structure, while addressing the structure learning problem. In addition, three main toolkits such as Ensembl, TFSearch and TRANSFAC have been used to determine the false positive edges and verify reliability of regulatory relationships. The experimental results show that by integrating biological knowledge it could enhance the precision results and reduce the number of false positive edges in the trained gene regulatory network. Institute of Research Management and Innovation (IRMI) 2015 Article PeerReviewed text en http://ir.uitm.edu.my/id/eprint/13580/1/AJ_FARZANA%20KABIR%20AHMAD%20SRJ%2015.pdf Kabir Ahmad, Farzana and Kamaruddin, Siti Sakira (2015) Research trends in microarray data analysis: modelling gene regulatory network by integrating transcription factors data / Farzana Kabir Ahmad and Siti Sakira Kamaruddin. Scientific Research Journal, 12 (1). pp. 39-50. ISSN 1675-7009
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Teknologi MARA
building	UiTM Institutional Repository
collection	Online Access
language	English
topic	Bayesian statistical decision theory Gene Regulatory Networks Transcription factors
spellingShingle	Bayesian statistical decision theory Gene Regulatory Networks Transcription factors Kabir Ahmad, Farzana Kamaruddin, Siti Sakira Research trends in microarray data analysis: modelling gene regulatory network by integrating transcription factors data / Farzana Kabir Ahmad and Siti Sakira Kamaruddin
description	The invention of microarray technology has enabled expression levels of thousands of genes to be monitored at once. This modernized approach has created large amount of data to be examined. Recently, gene regulatory network has been an interesting topic and generated impressive research goals in computational biology. Better understanding of the genetic regulatory processes would bring significant implications in the biomedical fields and many other pharmaceutical industries. As a result, various mathematical and computational methods have been used to model gene regulatory network from microarray data. Amongst those methods, the Bayesian network model attracts the most attention and has become the prominent technique since it can capture nonlinear and stochastic relationships between variables. However, structure learning of this model is NP-hard and computationally complex as the number of potential edges increase drastically with the number of genes. In addition, most of the studies only focused on the predicted results while neglecting the fact that microarray data is a fragmented information on the whole biological process. Hence, this study proposed a network-based inference model that combined biological knowledge in order to verify the constructed gene regulatory relationships. The gene regulatory network is constructed using Bayesian network based on low-order conditional independence approach. This technique aims to identify from the data the dependencies to construct the network structure, while addressing the structure learning problem. In addition, three main toolkits such as Ensembl, TFSearch and TRANSFAC have been used to determine the false positive edges and verify reliability of regulatory relationships. The experimental results show that by integrating biological knowledge it could enhance the precision results and reduce the number of false positive edges in the trained gene regulatory network.
format	Article
author	Kabir Ahmad, Farzana Kamaruddin, Siti Sakira
author_facet	Kabir Ahmad, Farzana Kamaruddin, Siti Sakira
author_sort	Kabir Ahmad, Farzana
title	Research trends in microarray data analysis: modelling gene regulatory network by integrating transcription factors data / Farzana Kabir Ahmad and Siti Sakira Kamaruddin
title_short	Research trends in microarray data analysis: modelling gene regulatory network by integrating transcription factors data / Farzana Kabir Ahmad and Siti Sakira Kamaruddin
title_full	Research trends in microarray data analysis: modelling gene regulatory network by integrating transcription factors data / Farzana Kabir Ahmad and Siti Sakira Kamaruddin
title_fullStr	Research trends in microarray data analysis: modelling gene regulatory network by integrating transcription factors data / Farzana Kabir Ahmad and Siti Sakira Kamaruddin
title_full_unstemmed	Research trends in microarray data analysis: modelling gene regulatory network by integrating transcription factors data / Farzana Kabir Ahmad and Siti Sakira Kamaruddin
title_sort	research trends in microarray data analysis: modelling gene regulatory network by integrating transcription factors data / farzana kabir ahmad and siti sakira kamaruddin
publisher	Institute of Research Management and Innovation (IRMI)
publishDate	2015
url	http://ir.uitm.edu.my/id/eprint/13580/ http://ir.uitm.edu.my/id/eprint/13580/1/AJ_FARZANA%20KABIR%20AHMAD%20SRJ%2015.pdf
first_indexed	2023-09-18T22:50:03Z
last_indexed	2023-09-18T22:50:03Z
_version_	1777417483325538304

Research trends in microarray data analysis: modelling gene regulatory network by integrating transcription factors data / Farzana Kabir Ahmad and Siti Sakira Kamaruddin

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