Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICTAI.2006.74
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
Source: 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. https://doi.org/10.1109/ICTAI.2006.74
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
URI: http://scholarbank.nus.edu.sg/handle/10635/43257
ISBN: 0769527280
ISSN: 10823409
DOI: 10.1109/ICTAI.2006.74
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

5
checked on Jan 16, 2018

Page view(s)

66
checked on Jan 14, 2018

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.