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

核心 D：定量生物学：生物统计学、生物信息学和计算

• 批准号: 9301104
• 负责人: MARTYN T SMITH
• 金额: $ 18.82万
• 依托单位: UNIVERSITY OF CALIFORNIA BERKELEY
• 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/9301104
• 关键词: Address; Area; Bioinformatics; Biological; Biology; Biometry; Collaborations; Communities; Computational Biology; Consensus; Consult; Data; Data Collection; Data Set; Databases; Development; Disease; Environment; Environmental Exposure; Epidemiology; Equilibrium; Etiology; Evolution; Experimental Designs; Genome; Goals; Homologous Gene; Knowledge; Measurement; Measures; Metadata; Methodology; Methods; Modeling; Online Systems; Pathway interactions; Pattern; Persons; Poison; Proliferating; Proteome; Research Infrastructure; Research Personnel; Research Project Grants; Resources; Sampling; Soil; System; Techniques; Technology; Toxin; Work; Yeasts; base; data archive; data mining; effective intervention; experimental analysis; high throughput technology; method development; outreach; potential biomarker; programs; tool

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.

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，并支持生物信息学解决方案和数据库访问的网络传播。这些项目的最具体目标是产生高维生物和暴露数据，并且通常涉及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相互作用的复杂问题。这些高维数据集的特点是对每个单元（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发展，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论专家围绕复杂问题和高维数据背景下的最优估计和推理的一套核心原则形成了共识。具体来说，共识倾向于使用（如果可能的话）：具有鲁棒性的半参数局部有效估计 推断和开发最佳方法，用于将统计结果整合到现有元数据中，以建议相关的生物途径和网络。应用这种方法将使分析能够合并不同的数据，以查询相关毒素和可能的相关疾病中的相似模式/途径，从而充分利用该计划生成的数据。为了实施这种方法，定量生物学核心将提供对计算环境的访问，该环境适合为数据挖掘和基于重采样的推理而开发的计算密集型方法。由于数据收集的规模以及收敛到通用方法的愿望，我们的计划需要一个更加集中的系统，该系统既可以存档数据，也可以向该核心提供共享，提供有关元数据/注释访问的指导，以及利用这些数据在现有假设的监管网络上找到我们结果的套印的例程。核心还将开发工具来查找和比较途径，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共享我们的方法学专业知识，并最终作为在一般科学界中推广的工具。