Core D: Quantitative Biology: Biostatistics, Bioinformatics, and Computation

定量生物学核心的目的是为研究人员提供生物统计学/计算生物学和生物信息学方面的咨询支持，并支持基于网络的生物信息学解决方案和数据库访问的传播。项目的大多数具体目标产生高维生物和暴露数据，并且通常涉及复杂的问题，解决环境暴露与基因组、蛋白质组和其他高通量技术的高维测量之间可能的相互作用。这些高维数据集的特征在于对每个单元（例如人，酵母培养物，土壤群落）进行数千次测量。核心D反映了生物统计学和生物信息学领域的一种演变，即开发既可以在高维数据集中找到模式，又可以为这些模式提供适当的统计推断的方法。我们的项目研究人员和方法学专家已经就复杂问题和高维数据背景下的最优估计和推理形成了一套核心原则。具体来说，共识倾向于使用（如果可能）：半参数局部有效估计与鲁棒 推断和开发用于将统计结果整合到现有元数据中的最佳方法，以提出相关的生物途径和网络。应用这种方法将使分析能够纳入不同的数据，以查询相关毒素和可能相关疾病的类似模式/途径，从而充分利用该方案产生的数据。为了实现这种方法，定量生物学核心将提供一个计算环境，该环境适合于为数据挖掘和基于重新采样的推理开发的计算密集型方法。 由于数据收集的规模以及融合到一个通用方法的愿望，我们的计划需要一个更集中的系统，既可以存档数据，提供共享到这个核心，指导访问元数据/注释和例程，利用这些数据来找到我们的结果在现有的假设监管网络上的叠印。该核心还将开发工具来寻找和比较途径，并创建和维护一个基于网络的系统，该系统将允许与项目研究人员有效地分享我们的方法学专业知识，并最终作为在一般科学界推广的工具。

The purpose of the Quantitative Biology Core is to provide investigators with consultative support in biostatistics/computational biology and bioinformatics, and to support web-based dissemination of bioinformatic solutions and database access. Most specific aims with the projects produce high-dimensional biological and exposure data, and often involve complicated questions addressing the possible interaction of environmental exposures and high-dimensional measures of the genome, proteome, and other high throughput technologies. These high-dimensional data sets are characterized by many thousands of measurements made on each unit (e.g. person, yeast culture, soil community). Core D reflects an evolution in the field of biostatistics and bioinformatics towards developing methodologies that can both find patterns in high dimensional data sets as well as providing proper statistical inference for these patterns. A consensus among our project researches and the methodological experts has formed around a set of core principles regarding optimal estimation and inference in the context of complicated questions and high-dimensional data. Specifically, the consensus favors using (when possible): semi-parametric locally efficient estimation with robust inference and the development of optimal methods used to integrate the statistical results into existing metadata to suggest relevant biological pathways and networks. Applying this approach will enable analyses to incorporate diverse data to query similar patterns/pathways in both related toxins and possible related diseases thus substantially leveraging data generated by the Program. To implement this methodology, the Quantitative Biology Core will provide access to a computational environment that lends itself to the computationally intensive methods developed for data mining and re-sampling based inference. Because of the scale of the data collection as well as the desirability of converging to a general methodology, our Program requires a more centralized system that can both archive data for, provide sharing to this Core, guidance on the access of metadata/annotation and routines for leveraging such data to find overprinting of our results on existing hypothesized regulatory networks. The Core will also develop tools to find and compares pathway, and create and maintain a web-based system that will allow for both efficient sharing of our methodological expertise with the project researchers and ultimately serve as a tool for outreach among the general scientific community.