Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.14/119462
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- Title
- A 2-stage approach for inferring gene regulatory networks using dynamic bayesian networks
- Related
- IEEE International Conference on Bioinformatics and Biomedicine (1 - 4 November 2009 : Washington, D.C.)
- Related
- BIBM 2009 : 2009 IEEE International Conference on Bioinformatics and Biomedicine : Washington, D.C., USA : 1-4 November 2009, p.166-169
- DOI
- 10.1109/BIBM.2009.87
- Publisher
- Los Alamitos, Calif : IEEE Computer Society
- Date
- 2009
- FoR/RFCD Code(s)
-
060100 Biochemistry and Cell Biology
080100 Artificial Intelligence and Image Processing
- Author/Creator
- Shermin, Akther
- Author/Creator
- Orgun, Mehmet A
- Description
- 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.
- Description
- 4 page(s)
- Subject Keyword
- 060100 Biochemistry and Cell Biology
- Subject Keyword
- 080100 Artificial Intelligence and Image Processing
- Resource Type
- conference paper
- Organisation
- Macquarie University. Dept. of Computing
- Identifier
- http://hdl.handle.net/1959.14/119462
- Identifier
- ISBN:9780769538853
- Identifier
- mq-rm-2009004987
- Language
- eng
- Full Text
- Reviewed
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