School of Computer Science, University of Windsor, 401, Sunset Avenue, Windsor, ON N9CB3P4, Canada.
IEEE/ACM Trans Comput Biol Bioinform. 2011 Mar-Apr;8(2):326-34. doi: 10.1109/TCBB.2010.98.
This paper demonstrates the use of qualitative probabilistic networks (QPNs) to aid Dynamic Bayesian Networks (DBNs) in the process of learning the structure of gene regulatory networks from microarray gene expression data. We present a study which shows that QPNs define monotonic relations that are capable of identifying regulatory interactions in a manner that is less susceptible to the many sources of uncertainty that surround gene expression data. Moreover, we construct a model that maps the regulatory interactions of genetic networks to QPN constructs and show its capability in providing a set of candidate regulators for target genes, which is subsequently used to establish a prior structure that the DBN learning algorithm can use and which 1) distinguishes spurious correlations from true regulations, 2) enables the discovery of sets of coregulators of target genes, and 3) results in a more efficient construction of gene regulatory networks. The model is compared to the existing literature using the known gene regulatory interactions of Drosophila Melanogaster.
本文展示了如何将定性概率网络 (QPN) 应用于动态贝叶斯网络 (DBN)，以帮助 DBN 从微阵列基因表达数据中学习基因调控网络的结构。我们进行了一项研究，表明 QPN 定义了单调关系，能够以较少受到基因表达数据周围许多不确定性源影响的方式识别调控相互作用。此外，我们构建了一个将遗传网络的调控相互作用映射到 QPN 结构的模型，并展示了其为靶基因提供一组候选调控因子的能力，这随后被用于建立 DBN 学习算法可以使用的先验结构，该结构 1）区分虚假相关性和真实调控，2）能够发现靶基因的核心调控因子集，3）导致基因调控网络的构建更加高效。该模型使用果蝇已知的基因调控相互作用与现有文献进行了比较。
