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A Scalable approach for inferring transcriptional regulation in the yeast cell cycle

Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.14/160391

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Title: A Scalable approach for inferring transcriptional regulation in the yeast cell cycle
Related: ACM Conference on Bioinformatics, Computational Biology and Biomedicine (2nd : 2011) (1 - 3 August 2011 : Chicago, IL)
Related: BCB '11 Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine, p.345-349
DOI: 10.1145/2147805.2147848
Publisher: New York : ACM
Date: 2011
Author/Creator: Shermin, Akther
Author/Creator: Jamil, Hasan
Author/Creator: Orgun, Mehmet A
Description: The high complexity in the gene regulation mechanism and the prevalent noise in high-throughput detection experiments are considered to be the two major obstacles in discovering transcriptional regulation with high accuracy from experimental gene expression data. In this paper, we study a model based on dynamic Bayesian networks to predict gene regulation by integrating transcription factor binding site data and proteinprotein interaction data with gene expression data. The knowledge of genetic interactions between proteins and the presence of transcription factors binding site at the promoter region of a gene have been used to restrict the number of potential regulators of each gene. We show the effectiveness of combining multiple data sources in the prediction of transcriptional regulation through the analysis of Saccharomyces cerevisiae (Yeast) cell cycle data. Experiments conducted on real microarray datasets show that the proposed model is significantly more efficient and topologically more accurate compared to other existing models based on dynamic Bayesian networks. We also demonstrate the scalability of the proposed model through the analysis of a large dataset with a sustainable performance level.
Description: 5 page(s)
Subject Keyword: Dynamic Bayesian Networks
Subject Keyword: Transcriptional Regulation
Subject Keyword: Binding Site Data
Subject Keyword: Protein-Protein Interaction Data
Subject Keyword: Microarray
Resource Type: conference paper
Organisation: Macquarie University. Dept. of Computing

Identifier: http://hdl.handle.net/1959.14/160391
Identifier: ISBN:9781450307963
Identifier: mq_res-20120316-153549
Language: eng
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