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: 8461220
• 负责人: MARTYN T SMITH
• 金额: $ 19.13万
• 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/8461220
• 关键词: Address; Archives; Area; Bioinformatics; Biological; Biological Markers; 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; Solutions; System; Techniques; Technology; Toxin; Work; Yeasts; base; data mining; effective intervention; experimental analysis; high throughput technology; method development; outreach; 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，并支持基于网络的生物信息学解决方案的传播和数据库访问。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的复杂问题。这些高维数据集的特点是对每个单位（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向开发既可以在高维数据集中发现模式又可以为这些模式提供适当统计推断的方法的发展。在复杂问题和高维数据的背景下，我们的项目研究人员和方法专家围绕一套关于最佳估计和推理的核心原则形成了共识。具体来说，共识倾向于使用（在可能的情况下）：具有鲁棒性的半参数局部有效估计