Department of Mathematics, Wake Forest University, Winston-Salem, North Carolina 27109, USA.
BMC Bioinformatics. 2012 Jun 11;13 Suppl 9(Suppl 9):S6. doi: 10.1186/1471-2105-13-S9-S6.
Often protein (or gene) time-course data are collected for multiple replicates. Each replicate generally has sparse data with the number of time points being less than the number of proteins. Usually each replicate is modeled separately. However, here all the information in each of the replicates is used to make a composite inference about signal networks. The composite inference comes from combining well structured Bayesian probabilistic modeling with a multi-faceted Markov Chain Monte Carlo algorithm. Based on simulations which investigate many different types of network interactions and experimental variabilities, the composite examination uncovers many important relationships within the networks. In particular, when the edge's partial correlation between two proteins is at least moderate, then the composite's posterior probability is large.
通常会为多个重复收集蛋白质（或基因）时间过程数据。每个重复通常具有稀疏数据，时间点的数量少于蛋白质的数量。通常每个重复都是单独建模的。但是，这里使用每个重复中的所有信息来对信号网络进行综合推断。综合推断来自将结构良好的贝叶斯概率建模与多方面的马尔可夫链蒙特卡罗算法相结合。基于对许多不同类型的网络相互作用和实验可变性进行模拟的结果，综合检查揭示了网络中的许多重要关系。特别是，当两个蛋白质之间的边缘部分相关系数至少适中时，那么组合的后验概率就很大。
