将生物学信息转化为线性模型：途径和基因选择的贝叶斯方法
Incorporating biological information into linear models: A Bayesian approach to the selection of pathways and genes
摘要：
多年来积累的大量生物学知识使研究人员能够识别各种生化相互作用并定义不同的途径家族。人们对识别特定生物过程中涉及的途径和途径要素越来越感兴趣。例如，药物发现工作的重点是确定生物标志物以及与疾病相关的途径。我们提出了一个贝叶斯模型，解决了这个问题，将信息的途径和基因网络的DNA微阵列数据的分析。这些信息用于定义路径摘要、指定先验分布以及构造MCMC移动以拟合模型。我们举例说明的方法与应用程序的基因表达数据删失的生存结果。除了识别否则会被遗漏的标记物并提高预测准确性之外，将现有生物学知识整合到分析中还可以更好地理解潜在的分子过程。
英文摘要：
The vast amount of biological knowledge accumulated over the years has allowed researchers to identify various biochemical interactions and define different families of pathways. There is an increased interest in identifying pathways and pathway elements involved in particular biological processes. Drug discovery efforts, for example, are focused on identifying biomarkers as well as pathways related to a disease. We propose a Bayesian model that addresses this question by incorporating information on pathways and gene networks in the analysis of DNA microarray data. Such information is used to define pathway summaries, specify prior distributions, and structure the MCMC moves to fit the model. We illustrate the method with an application to gene expression data with censored survival outcomes. In addition to identifying markers that would have been missed otherwise and improving prediction accuracy, the integration of existing biological knowledge into the analysis provides a better understanding of underlying molecular processes.
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