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

Title	Inferring Gene Regulatory Networks from Gene Expression Data by a Dynamic Bayesian Network-based Model
File name	L.E._Chai2012-Inferring_Gene_Regulatory_Networks_from_Gene_Expression_Data_by_a_Dynamic_Bayesian_Network-based_Model.pdf
File size	215173
SHA1	68c7a2e786db8e5caecdf5b9e0e79d7052315b67
Content type	Adobe PDF

Description

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

relationships