Development of Bayesian Graphical Models for Next-Generation Genetic Studies

在过去的二十年里，图形模型一直是用于分析复杂结构的高维数据的最有效的统计工具之一(Friedman，2004)。图形模型提供了一个概率框架，用于进行推理并表示我们拥有的关于这些复杂结构化数据的知识。在生物学研究中，尤其是在基因组学、蛋白质组学、代谢组学、转录组学等新兴的组学学科中，数据往往来自复杂的高通量实验和复杂的实验设计。图形模型可以表示这些复杂的生物问题，导致相对简单和易于处理的计算算法来获得感兴趣的量。尽管有明显的优势，但在生物学研究中，无论是在频数框架下还是在贝叶斯框架下使用图形模型都相对较少。在组学学科中，这可以部分解释为将图形模型适应高维数据的计算困难，以及缺乏贝叶斯图形模型的通用理论框架。

Graphical models have been one of the most efficient statistical tools used in the last twenty years for the analysis of complex structured high-dimensional data (Friedman, 2004). Graphical models provide a probabilistic framework for making inference and representing the knowledge that we have about these complex structured data. In biological research and more particularly in the emerging ‘omics disciplines such as genomics, proteomics, metabolomics, transcriptomics, data are often generated from complex high-throughput experiments and from complex experimental designs. Graphical models can represent these complex biological problems, leading to relatively simple and tractable computational algorithms to obtain the quantities of interest. Despite its obvious advantages the use of graphical models under either the frequentist or Bayesian framework remains relatively rare in biological research. In ‘omics disciplines, this could be partly explained by the computational difficulties in fitting graphical models to high-dimensional data and also the lack of a general theoretical framework for Bayesian graphical models.