A 2-stage approach for inferring gene regulatory networks using dynamic Bayesian networks

Akther Shermin; Mehmet A. Orgun

doi:10.1109/BIBM.2009.87

A 2-stage approach for inferring gene regulatory networks using dynamic Bayesian networks

Akther Shermin^*, Mehmet A. Orgun

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

6 Citations (Scopus)

17 Downloads (Pure)

Abstract

The inference of Gene Regulatory networks (GRN) from microarrray data suffers from the low accuracy and the excessive computation time. Biological domain knowledge of the cellular process, from which the data is generated, is believed to be effective in addressing such challenges. In this paper, we have used two biological features of gene regulation of yeast cell cycle: 1) a high proportion of the Cell Cycle Regulated genes are periodically expressed, and 2) genes are both co-expressed and co-regulated. Together with the computational implementation of these features, we have learnt regulators of both individual and co-expressed genes using Dynamic Bayesian Networks. The proposed 2-stage GRN model has been found to be more computationally efficient and topologically accurate compared to other existing models.

Original language	English
Title of host publication	2009 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2009
Place of Publication	Los Alamitos, CA
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	166-169
Number of pages	4
ISBN (Print)	9780769538853
DOIs	https://doi.org/10.1109/BIBM.2009.87
Publication status	Published - 2009
Event	2009 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2009 - Washington, D.C., United States Duration: 1 Nov 2009 → 4 Nov 2009

Other

Other	2009 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2009
Country/Territory	United States
City	Washington, D.C.
Period	1/11/09 → 4/11/09

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10.1109/BIBM.2009.87

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title = "A 2-stage approach for inferring gene regulatory networks using dynamic Bayesian networks",

abstract = "The inference of Gene Regulatory networks (GRN) from microarrray data suffers from the low accuracy and the excessive computation time. Biological domain knowledge of the cellular process, from which the data is generated, is believed to be effective in addressing such challenges. In this paper, we have used two biological features of gene regulation of yeast cell cycle: 1) a high proportion of the Cell Cycle Regulated genes are periodically expressed, and 2) genes are both co-expressed and co-regulated. Together with the computational implementation of these features, we have learnt regulators of both individual and co-expressed genes using Dynamic Bayesian Networks. The proposed 2-stage GRN model has been found to be more computationally efficient and topologically accurate compared to other existing models.",

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year = "2009",

doi = "10.1109/BIBM.2009.87",

language = "English",

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booktitle = "2009 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2009",

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note = "2009 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2009 ; Conference date: 01-11-2009 Through 04-11-2009",

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Shermin, A & Orgun, MA 2009, A 2-stage approach for inferring gene regulatory networks using dynamic Bayesian networks. in 2009 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2009., 5341827, Institute of Electrical and Electronics Engineers (IEEE), Los Alamitos, CA, pp. 166-169, 2009 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2009, Washington, D.C., United States, 1/11/09. https://doi.org/10.1109/BIBM.2009.87

A 2-stage approach for inferring gene regulatory networks using dynamic Bayesian networks. / Shermin, Akther; Orgun, Mehmet A.

2009 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2009. Los Alamitos, CA : Institute of Electrical and Electronics Engineers (IEEE), 2009. p. 166-169 5341827.

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

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AB - The inference of Gene Regulatory networks (GRN) from microarrray data suffers from the low accuracy and the excessive computation time. Biological domain knowledge of the cellular process, from which the data is generated, is believed to be effective in addressing such challenges. In this paper, we have used two biological features of gene regulation of yeast cell cycle: 1) a high proportion of the Cell Cycle Regulated genes are periodically expressed, and 2) genes are both co-expressed and co-regulated. Together with the computational implementation of these features, we have learnt regulators of both individual and co-expressed genes using Dynamic Bayesian Networks. The proposed 2-stage GRN model has been found to be more computationally efficient and topologically accurate compared to other existing models.

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A 2-stage approach for inferring gene regulatory networks using dynamic Bayesian networks

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