Bayesian modeling of complex metabolic pathways
Bayesian modeling of complex metabolic pathways
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DOI：
10.1159/000073736
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发表时间：
2003-01-01
期刊：
影响因子：
1.8
通讯作者：
Thomas, DC
中科院分区：
文献类型：
--
作者：
Conti, DV;Cortessis, V;Thomas, DC
许多慢性疾病是一系列复杂的生化反应的结果，这些反应涉及暴露于各种环境因子，由许多不同的基因代谢。常规流行病学分析往往依赖于标准列联表或逻辑回归方法，通常集中在一个变量的时间或成对组合。我们认为这种方法的两个统计替代方案，一个基于贝叶斯模型平均，一个基于生化途径的药代动力学建模。这些方法说明使用的数据从结直肠息肉的病例对照研究吸烟和消费做得好的红肉，都被视为杂环胺和多环芳烃的来源。新的分析结构的方式，试图利用这些类别的化合物和调节这些途径的各种基因的代谢的先验知识。版权所有（C）2003 S. Karger AG，巴塞尔。
Many chronic diseases are the result of a complex sequence of biochemical reactions involving exposures to various environmental agents, metabolized by a number of different genes. Routine epidemiologic analyses of such associations have tended to rely on standard contingency table or logistic regression methods, typically focusing on one variable at a time or pairwise combinations. We consider two statistical alternatives to this approach, one based on Bayesian model averaging, one based on pharmacokinetic modeling of the biochemical pathways. These approaches are illustrated using data from a case-control study of colorectal polyps in relation to tobacco smoking and consumption of well done red meat, both viewed as sources of heterocyclic amines and polycyclic aromatic hydrocarbons. The new analyses are structured in a manner that attempts to take advantage of prior knowledge of the metabolism of these classes of compounds and the various genes that regulate these pathways. Copyright (C) 2003 S. Karger AG, Basel.