Statistical Models In Toxicology And Biochemistry

毒理学和生物化学的统计模型

• 批准号: 7327698
• 负责人: Christopher J Portier
• 金额: --
• 依托单位: NATIONAL INSTITUTE OF ENVIRONMENTAL HEALTH SCIENCES
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/7327698
• 关键词: Statistical; Models; Toxicology; Biochemistry

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.

该项目的重点是：（1）制定将“尖端”研究结果纳入未来风险评估的方法;（2）制定设计研究的方法，以改进风险估计，特别是在涉及机械数据时;（3）制定评估照射、剂量-反应形状和效力的方法;（4）制定涉及多种机制的混合物的评价方法;（5）制定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，让监管机构直接参与;（7）通过将理论发展与准确和方便的计算方法仔细联系起来，实际改进随机过程;（8）与NIEHS内的研究小组和从事类似工作的研究小组合作，提高对疾病发病率的生物学认识;（9）通过假设检验和实验室研究，不断改进疾病发病率模型的生物学基础;（10）以科学上可信的方式将疾病发病率模型与毒理学模型联系起来;（11）在模型的开发及其应用中使用最广泛的数据;（12）支持国家毒理学方案。 我们开发了一种定量的，统计学上合理的方法，用于分析可疑的基因调控网络，使用基因表达数据集。该方法是基于贝叶斯网络，并提供了一种手段，直接量化基因表达网络和测试假设的基因之间的联系，在这个网络。