Please use this identifier to cite or link to this item:
|Title:||Model gene network by semi-fixed Bayesian network|
|Authors:||Liu, T.-F. |
Semi-fixed structure EM learning algorithm
|Source:||Liu, T.-F., Sung, W.-K., Mittal, A. (2006). Model gene network by semi-fixed Bayesian network. Expert Systems with Applications 30 (1) : 42-49. ScholarBank@NUS Repository. https://doi.org/10.1016/j.eswa.2005.09.044|
|Abstract:||Gene networks describe functional pathways in a given cell or tissue, representing processes such as metabolism, gene expression regulation, and protein or RNA transport. Thus, learning gene network is a crucial problem in the post genome era. Most existing works learn gene networks by assuming one gene provokes the expression of another gene directly leading to an over-simplified model. In this paper, we show that the gene regulation is a complex problem with many hidden variables. We propose a semi-fixed model to represent the gene network as a Bayesian network with hidden variables. In addition, an effective algorithm based on semi-fixed structure learning is proposed to learn the model. Experimental results and comparison with the-state-of-the-art learning algorithms on artificial and real-life datasets confirm the effectiveness of our approach. © 2005 Elsevier Ltd. All rights reserved.|
|Source Title:||Expert Systems with Applications|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Dec 14, 2017
WEB OF SCIENCETM
checked on Nov 18, 2017
checked on Dec 10, 2017
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.