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|Title:||Learning gene network using conditional dependence|
|Authors:||Liu, T.-F. |
Conditional relative entropy
|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. 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|
|Appears in Collections:||Staff Publications|
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