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化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，用于进行推理和表示我们对这些复杂结构化数据的知识。在生物学研究中，更具体地说，在新兴的组学学科如基因组学、蛋白质组学、代谢组学、转录组学中，数据通常是从复杂的高通量实验和复杂的实验设计中产生的。图形模型可以表示这些复杂的生物学问题，导致相对简单和易于处理的计算算法，以获得感兴趣的数量。尽管其明显的优势，使用图形模型下的频率或贝叶斯框架仍然相对较少的生物学研究。在组学学科中，这可以部分解释为将图形模型拟合到高维数据的计算困难，以及缺乏贝叶斯图形模型的一般理论框架。

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.