Statistical Models In Toxicology And Biochemistry

该项目的重点是：(1)开发将“尖端”研究成果纳入未来风险评估的方法；(2)开发改进风险评估的研究设计方法，特别是在涉及机械数据的情况下；(3)开发评估暴露、剂量-反应形状和效力的方法；(4)开发当涉及多种机制时评估混合物的方法；(5)开发协调癌症和非癌症健康风险评估的方法；(6)通过专家小组、同行审查和合作研究，直接让监管机构参与进来；(7)通过将理论发展与精确和方便的计算方法仔细联系起来，切实改进随机过程；(8)与NIEHS内的研究小组和从事类似工作的研究小组合作，以提高对疾病发病率的生物学理解；(9)通过假设检验和实验室研究，反复改进疾病发病率模型的生物学基础；(10)以科学可信的方式将疾病发病率模型与毒物动力学模型联系起来；(11)在模型的开发及其应用中使用最广泛的数据；(12)支持国家毒理学计划。 我们开发了一种定量的、统计上合理的方法，用于使用基因表达数据集分析可疑的基因调控网络。该方法基于贝叶斯网络，提供了一种直接量化基因表达网络和测试关于该网络中基因之间联系的假设的方法。 该方法目前正在应用于许多数据集，包括关于早期大脑发育和对乙酰氨基酚毒性的数据。

This Project focuses on (1) The development of methodology for incorporating "cutting-edge" research findings into future risk assessments; (2) The development of methods for designing studies to improve risk estimates, especially when mechanistic data is involved; (3) The development of methods for the evaluation of exposure, dose-response shape and potency; (4) The development of methods for evaluating mixtures when multiple mechanisms are involved; (5) The development of methods which harmonize cancer and non-cancer health risk assessments; (6) Direct engagement of the regulatory community through expert panels, peer review and collaborative research; (7) Practical improvement of stochastic processes through careful linkage of theoretical developments with computational methods that are accurate and convenient; (8) Collaboration with research groups within the NIEHS and research groups doing similar work to improve the biological understanding of disease incidence; (9) Iterative improvement of the biological basis for disease incidence models through a process of hypothesis testing and laboratory research; (10) Linkage of disease incidence models to toxicokinetics models in a scientifically credible manner; (11) Use of the broadest array of data in both the development of the model and its application; (12) Support of the National Toxicology Program. We developed a quantitative, statistically sound methodology for the analysis of suspected gene regulatory networks using gene expression data sets. The method is based on Bayesian networks and provides a means to directly quantify gene-expression networks and test hypotheses regarding the linkages between genes in this network. The method is currently being applied to a number of data sets including data on early brain development and acetaminophen toxicity.