Computational Biology Core

核心D项下提供的支助反映了以下研究的日益增长的趋势 从更传统的流行病学研究和简单的实验设计中获得环境暴露 高维生物学，其重点是“组学”技术和复杂问题的解决 环境暴露和基因组的高维测量之间可能的相互作用， 蛋白质组等。这些高维数据集的特征是许多(数千)个测量 仅在几个独立的单位(例如，人)上制作的。因此，核心D反映了 生物统计学领域致力于开发既能在高维数据中找到模式又能找到模式的方法 并为这些模式提供适当的统计推断。除了提供咨询服务外 传统流行病学实验设计和分析问题，核心D将重点放在 为本方案的项目提供最相关和最严谨的统计技术。有了新的“经济” 技术，生物学已经进入了一个新的更具经验性的阶段，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(生物重复数以十计)。有了这些技术， 也出现了大量提出的方法来寻找具有生物意义的模式，并且通常 几乎没有提供理论来指导它们的相对价值。此核心的目标是提供项目 研究人员使用最好的技术，软件来帮助实现它们，计算 一个可以处理大型数据集上的计算机密集型方法的环境，最重要的是， 对这些程序估计的参数进行严格的统计推断。属性的子集 与高层面生物/流行病学数据扩散有关的发展，特别是 与该方案相关的有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，5)生物元数据和系统生物学。此外， 内核将提供对计算环境的访问，该环境适合计算密集型 开发了用于数据挖掘和基于重采样的推理的方法。

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.