File Entry: Inferring Gene Regulatory Networks from Gene Expression Data by a Dynamic Bayesian Network-based Model

Created: 2012-05-11 02:20:28
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Version created on: 2012-05-11 02:20:28

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 Enabled by recent advances in bioinformatics, the inference of gene

regulatory networks (GRNs) from gene expression data has garnered much
interest from researchers. This is due to the need of researchers to understand the
dynamic behavior and uncover the vast information lay hidden within the
networks. In this regard, dynamic Bayesian network (DBN) is extensively used to
infer GRNs due to its ability to handle time-series microarray data and modeling
feedback loops. However, the efficiency of DBN in inferring GRNs is often
hampered by missing values in expression data, and excessive computation time
due to the large search space whereby DBN treats all genes as potential regulators
for a target gene. In this paper, we proposed a DBN-based model with missing
values imputation to improve inference efficiency, and potential regulators
detection which aims to lessen computation time by limiting potential regulators
based on expression changes. The performance of the proposed model is assessed
by using time-series expression data of yeast cell cycle. The experimental results showed reduced computation time and improved efficiency in detecting gene-gene

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