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Title: Learning gene network using conditional dependence
Authors: Liu, T.-F. 
Sung, W.-K. 
Keywords: Bayesian networks
Conditional dependence
Conditional relative entropy
Gene network
Regulatory complex
Issue Date: 2006
Citation: Liu, T.-F.,Sung, W.-K. (2006). Learning gene network using conditional dependence. Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI : 800-804. ScholarBank@NUS Repository.
Abstract: Gene network, conventionally, is learned by studying the pairwise correlation of the microarray expression profiles of different genes. This approach, however, is reported to be effective only for learning a small portion of the regulatory pairs due to the complexity of the gene regulatory system. In this paper, through studying the conditional dependence of the gene expression profiles, a new algorithm, Conditional Dependence Learning algorithm, is proposed which considers three additional factors: (1) the collaboration among regulators, (2) the formation of regulatory complex, and (3) the variable time delay to learn the gene network. Experiments on both artificial and real-life gene expression datasets validate the goodness of the algorithm. © 2006 IEEE.
Source Title: Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI
ISBN: 0769527280
ISSN: 10823409
DOI: 10.1109/ICTAI.2006.74
Appears in Collections:Staff Publications

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