DS '02 Proceedings of the 5th International Conference on Discovery Science
Inferring Gene Regulatory Networks using Differential Evolution with Local Search Heuristics
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Using dynamic bayesian networks to infer gene regulatory networks from expression profiles
Proceedings of the 2009 ACM symposium on Applied Computing
A 2-Stage Approach for Inferring Gene Regulatory Networks Using Dynamic Bayesian Networks
BIBM '09 Proceedings of the 2009 IEEE International Conference on Bioinformatics and Biomedicine
Using a state-space model and location analysis to infer time-delayed regulatory networks
EURASIP Journal on Bioinformatics and Systems Biology
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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.