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- Title
Comparison of probabilistic Boolean network and dynamic Bayesian network approaches for inferring gene regulatory networks.
- Authors
Li, Peng; Zhang, Chaoyang; Perkins, Edward J; Gong, Ping; Deng, Youping
- Abstract
The regulation of gene expression is achieved through gene regulatory networks (GRNs) in which collections of genes interact with one another and other substances in a cell. In order to understand the underlying function of organisms, it is necessary to study the behavior of genes in a gene regulatory network context. Several computational approaches are available for modeling gene regulatory networks with different datasets. In order to optimize modeling of GRN, these approaches must be compared and evaluated in terms of accuracy and efficiency.
- Publication
BMC bioinformatics, 2007, Vol 8 Suppl 7, pS13
- ISSN
1471-2105
- Publication type
Journal Article
- DOI
10.1186/1471-2105-8-S7-S13