Gippsland School of Information Technology, Monash University, Melbourne, Australia.
BMC Bioinformatics. 2012 Jun 13;13:131. doi: 10.1186/1471-2105-13-131.
Dynamic Bayesian network (DBN) is among the mainstream approaches for modeling various biological networks, including the gene regulatory network (GRN). Most current methods for learning DBN employ either local search such as hill-climbing, or a meta stochastic global optimization framework such as genetic algorithm or simulated annealing, which are only able to locate sub-optimal solutions. Further, current DBN applications have essentially been limited to small sized networks.
To overcome the above difficulties, we introduce here a deterministic global optimization based DBN approach for reverse engineering genetic networks from time course gene expression data. For such DBN models that consist only of inter time slice arcs, we show that there exists a polynomial time algorithm for learning the globally optimal network structure. The proposed approach, named GlobalMIT+, employs the recently proposed information theoretic scoring metric named mutual information test (MIT). GlobalMIT+ is able to learn high-order time delayed genetic interactions, which are common to most biological systems. Evaluation of the approach using both synthetic and real data sets, including a 733 cyanobacterial gene expression data set, shows significantly improved performance over other techniques.
Our studies demonstrate that deterministic global optimization approaches can infer large scale genetic networks.
动态贝叶斯网络（DBN）是建模各种生物网络的主流方法之一，包括基因调控网络（GRN）。目前学习 DBN 的大多数方法都采用局部搜索（如爬山）或元随机全局优化框架（如遗传算法或模拟退火），这些方法只能找到次优解。此外，目前的 DBN 应用基本上仅限于小型网络。
为了克服上述困难，我们在这里引入了一种基于确定性全局优化的 DBN 方法，用于从时间序列基因表达数据中反向工程基因网络。对于仅由时间片之间的弧组成的这种 DBN 模型，我们证明存在学习全局最优网络结构的多项式时间算法。所提出的方法称为 GlobalMIT+，它采用了最近提出的名为互信息测试（MIT）的信息论评分度量。GlobalMIT+能够学习常见于大多数生物系统的高阶时滞遗传相互作用。使用包括 733 个蓝藻基因表达数据集在内的合成和真实数据集评估该方法，表明其性能明显优于其他技术。
我们的研究表明，确定性全局优化方法可以推断大规模遗传网络。
