Please use this identifier to cite or link to this item: https://doi.org/10.1142/S0218213006002710
Title: Learning gene network using time-delayed Bayesian network
Authors: Liu, T.-F. 
Sung, W.-K. 
Mittal, A.
Keywords: Bayesian networks
Causal relationship
Gene network
Learning by modification
Mutual information
Time-delayed bayesian network
Issue Date: 2006
Source: Liu, T.-F., Sung, W.-K., Mittal, A. (2006). Learning gene network using time-delayed Bayesian network. International Journal on Artificial Intelligence Tools 15 (3) : 353-370. ScholarBank@NUS Repository. https://doi.org/10.1142/S0218213006002710
Abstract: Exact determination of a gene network is required to discover the higher-order structures of an organism and to interpret its behavior. Most research work in learning gene networks either assumes that there is no time delay in gene expression or that there is a constant time delay. This paper shows how Bayesian Networks can be applied to represent multi-time delay relationships as well as directed loops. The intractability of the network learning algorithm is handled by using an improved mutual information criterion. Also, a new structure learning algorithm, "Learning By Modification", is proposed to learn the sparse structure of a gene network. The experimental results on synthetic data and real data show that our method is more accurate in determining the gene structure as compared to the traditional methods. Even transcriptional loops spanning over the whole cell can be detected by our algorithm. © World Scientific Publishing Company.
Source Title: International Journal on Artificial Intelligence Tools
URI: http://scholarbank.nus.edu.sg/handle/10635/43061
ISSN: 02182130
DOI: 10.1142/S0218213006002710
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