School of Life Sciences and the State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Shatin, N. T., Hong Kong SAR, China.
Comput Biol Med. 2011 Sep;41(9):763-70. doi: 10.1016/j.compbiomed.2011.06.012. Epub 2011 Jul 8.
Bayesian network (BN) has been successfully used to infer the regulatory relationships of genes from microarray dataset. However, one major limitation of BN approach is the computational cost because the calculation time grows more than exponentially with the dimension of the dataset. In this paper, we propose a sub-space greedy search method for efficient Bayesian Network inference. Particularly, this method limits the greedy search space by only selecting gene pairs with higher partial correlation coefficients. Using both synthetic and real data, we demonstrate that the proposed method achieved comparable results with standard greedy search method yet saved ∼50% of the computational time. We believe that sub-space search method can be widely used for efficient BN inference in systems biology.
贝叶斯网络（BN）已成功用于从微阵列数据集推断基因的调控关系。然而，BN 方法的一个主要限制是计算成本，因为计算时间随着数据集的维度呈指数级增长。在本文中，我们提出了一种子空间贪婪搜索方法，用于有效的贝叶斯网络推断。特别是，该方法通过仅选择具有更高偏相关系数的基因对来限制贪婪搜索空间。使用合成和真实数据，我们证明了所提出的方法在获得与标准贪婪搜索方法相当的结果的同时，节省了约 50%的计算时间。我们相信子空间搜索方法可以广泛用于系统生物学中的有效 BN 推断。
