Computational Biology Core

计算生物学核心

• 批准号: 7089451
• 负责人: Mark J Vanderlaan
• 金额: $ 22.45万
• 依托单位: UNIVERSITY OF CALIFORNIA BERKELEY
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-04-01 至 2011-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7089451
• 关键词: biomedical facility; computational biology; computer program /software; environmental exposure; mathematics; statistics /biometry

The support provided under Core D reflect a growing trend in studies of environmental exposure from more traditional epidemiological studies and simple experimental designs to high-dimensional biology, with its emphasis on 'omic' technologies and complicated questions addressing the possible interaction of environmental exposures and high-dimensional measures of the genome, proteome, etc. These high-dimensional data sets are characterized by many (thousands) of measurements made on only a few independent units (e.g., people). Thus, the Core D reflects a parallel evolution in the field of biostatistics towards developing methodologies that can both find patterns in high dimensional data sets as well as providing proper statistical inference for these patterns. Besides offering consulting on traditional epidemiological experimental design and analysis questions, Core D will focus its efforts on providing the most relevant and rigorous statistical techniques to the Program's projects. With new 'omic' technologies, biology has entered a new more empirical phase where the goals of the research are ambitious (e.g., discovery of regulatory gene networks affected by particular environmental toxicants), but the sample sizes relatively small (biological replicates numbering in the tens). With these technologies, have come also a proliferation of proposed methods to find biologically meaningful patterns and typically little theory is provided to guide their relative worth. The goal of this Core is to provide the project researchers with the best techniques available, software to help implement them, a computational environment that can handle computer-intensive methods on large data sets and, most importantly, rigorous statistical inference for the parameters estimated by these procedures. A subset of the developments related to the proliferation of high-dimensional biological/epidemiological data particularly relevant to this proposal are 1) multiple testing, 2) machine-learning and loss-based estimation, 3) grouping algorithms methods, 4) causal inference and 5) biological metadata and systems biology. In addition, the 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.

核心项目D项下提供的支助反映了以下研究的增长趋势： 传统流行病学研究和简单的实验设计， 高维生物学，其重点是“组学”技术和复杂的问题解决 环境暴露和基因组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许多（数千）测量 仅在几个独立单元上进行（例如，人）。因此，核心D反映了一个平行的演变， 生物统计学领域的发展方法，既可以发现模式，在高维数据 集合，并为这些模式提供适当的统计推断。除了提供咨询， 传统的流行病学实验设计和分析问题，核心D将集中精力 为该计划的项目提供最相关和最严格的统计技术。新的“omic” 随着技术的发展，生物学已经进入了一个新的更加实证的阶段，研究的目标是 雄心勃勃（例如，发现受特定环境毒物影响的调控基因网络），但 样本量相对较小（生物学重复数为数十）。有了这些技术， 也出现了大量的方法来发现生物学上有意义的模式， 很少有理论能够指导它们的相对价值。该核心的目标是提供项目 研究人员与最好的技术，软件，以帮助实现他们，一个计算 环境，可以处理大型数据集上的计算机密集型方法，最重要的是， 通过这些程序估计的参数的严格统计推断。的子集 与高维生物/流行病学数据的扩散有关的发展， 与该建议相关的是1）多重测试，2）机器学习和基于损失的估计，3）分组 算法方法，4）因果推理和5）生物元数据和系统生物学。此外该 核心将提供对计算环境的访问， 为数据挖掘和基于重新采样的推理而开发的方法。