Core C Biostatistics and Modeling Research Support Core (Waters)

项目摘要（见说明）：生物统计学和建模核心为我们所有的研究项目和核心所利用的研究数据的实验设计，数据集成和预测建模提供了一个集中的计划。我们已经创建了一个多学科团队，拥有统计学，生物信息学，建模和计算机科学方面的专业知识，为该研究支持核心提供广泛的支持能力。在该战略调整计划的头三年，核心C在研究的各个方面提供了宝贵的支持。从实验设计到多元整合，从生物信息学到监管网络，从数据管理到定制软件解决方案，我们的Core促进了研究项目的科学进步，并实现了多个研究项目的数据集成。在接下来的五年里，我们建议继续支持该计划，提供复杂的数据分析，并通过三个主要具体目标将我们的努力扩展到更具预测性的计算建模; 1）生物统计学支持，以促进化学混合物的暴露（来源）与表型（结果）的联系，2）混合物的机制评估的预测建模和信息学，以及3）用于数据处理和集成的定制软件解决方案。核心C确保所有研究项目和核心的统计学稳健的实验设计、标准化的数据管道、数据集成和结果解释，以确保对暴露、剂量、反应和表型的稳健测量，并实现基于科学的风险评估的源与结果的联系。毒理学学生和统计学和生物信息学博士后研究员的多学科培训也确保了负责保护人类健康和环境免受有害物质风险的下一代研究人员和专业人员将拥有分析和解释自己数据的技能。

PROJECT SUMMARY (See instructions): The Biostatistics and Modeling Core provides a centralized plan for experimental design, data integration and predictive modeling of research data that is utilized by all of our Research Projects and Cores. We have created a multi-disciplinary team with expertise in statistics, bioinformatics, modeling and computer science to provide broad support capabilities in this Research Support Core. In the first three years of this SRP, Core C has provided invaluable support in all aspects of the research. From experimental design to multivariate integration, from bioinformatics to regulatory networks, and from data management to customized software solutions, our Core has facilitated scientific advancement in the research projects and enabled data integration across multiple research projects. In the next five years, we propose to continue our support of the program, providing sophisticated data analyses and expand our efforts into more predictive, computational modeling through three primary specific aims; 1) biostatistics support to facilitate linkage of exposure (source) to phenotype (outcome) for chemical mixtures, 2) predictive modeling and informatics for mechanistic evaluation of mixtures, and 3) customized software solutions for data processing and integrations. Core C ensures statistically robust experimental design, standardized data pipelines, data integration and results interpretation across all research projects and cores to ensure robust measurement of exposure, dose, response and phenotype and achieve source to outcome linkage for science-based risk assessment. Multidisciplinary training of toxicology students and postdoctoral fellows in statistics and bioinformatics also assures that the next generation of researchers and professionals tasked with protecting human health and the environment from the risks of hazardous substances will possess the skills to analyze and interpret their own data.