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|Title:||Model gene network by semi-fixed Bayesian network|
|Authors:||Liu, T.-F. |
Semi-fixed structure EM learning algorithm
|Citation:||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|
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