Inferring Dynamic Genetic Networks with Low Order Independencies
Inferring Dynamic Genetic Networks with Low Order Independencies
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DOI：
10.2202/1544-6115.1294
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发表时间：
2009-01-01
影响因子：
0.9
通讯作者：
Lebre, Sophie
中科院分区：
文献类型：
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作者：
Lebre, Sophie
在本文中，我们介绍了一种新的推理方法，动态遗传网络，这使得它可以面对一些时间测量n是远远小于基因的数量p.该方法是基于一个低阶的条件依赖图，我们在这里扩展的动态贝叶斯网络的情况下的概念。我们的大多数结果是基于与有向无环图（DAG）相关的图形模型理论。通过这种方式，我们定义了一个最小的DAG G，它精确地描述了过去过程中给出的全阶条件依赖关系。然后，面对大p和小n估计的情况下，我们提出了近似DAG G考虑低阶条件独立。引入了部分q阶条件依赖DAG G（（q）），并分析了它们的概率性质.一般来说，DAG G（（q））不同于DAG G，但仍然反映了稀疏网络（如遗传网络）的相关依赖事实。通过使用这种近似，我们提出了一种非贝叶斯推理方法，并证明了这种方法的有效性模拟和真实的数据分析。推理过程在R包'G1DBN'中实现，可从R存档（CRAN）免费获得。
In this paper, we introduce a novel inference method for dynamic genetic networks which makes it possible to face a number of time measurements n that is much smaller than the number of genes p. The approach is based on the concept of a low order conditional dependence graph that we extend here in the case of dynamic Bayesian networks. Most of our results are based on the theory of graphical models associated with the directed acyclic graphs (DAGs). In this way, we define a minimal DAG G which describes exactly the full order conditional dependencies given in the past of the process. Then, to face with the large p and small n estimation case, we propose to approximate DAG G by considering low order conditional independencies. We introduce partial qth order conditional dependence DAGs G((q)) and analyze their probabilistic properties. In general, DAGs G((q)) differ from DAG G but still reflect relevant dependence facts for sparse networks such as genetic networks. By using this approximation, we set out a non-Bayesian inference method and demonstrate the effectiveness of this approach on both simulated and real data analysis. The inference procedure is implemented in the R package 'G1DBN' freely available from the R archive (CRAN).