Data Mining

生物医学研究已成为数据密集型领域。突破性的技术，如微阵列， 现在比以往任何时候都更加强大和实惠，而最近推出的其他产品，如高吞吐量 流式细胞术、多重蛋白质分析、蛋白质阵列， 我们建议采用的其他一些平台来监测疫苗接种反应， 在这个研究项目的背景下产生的大量数据。数据挖掘核心的作用是 支持中心开展的研究，提供能够处理和 分析大量免疫分析数据，以及创新和开发新分析的能力 工具和方法。数据挖掘核心所进行的工作的基础将由一个 坚实的数据管理基础设施。此外，核心将统一跨项目的分析策略， 提供标准化的数据挖掘和生物统计分析管道。例如，我们的 生物信息学团队负责处理由每个微阵列产生的大量微阵列数据， 研究项目。此外，我们的生物统计团队根据本报告中提供的可行性数据， 应用程序已经设计了将用于询问免疫分析数据的分析策略。数据 挖掘核心还将提供尖端的生物信息学分析工具和策略， 满足多维分子分析方法所带来的独特挑战， 该计划将适用于疫苗反应的研究。为此，我们将依靠生物信息学团队， BIIR已经证明了其创新能力，正如其出版记录所示。 此外，我们也将指望我们的合作者的宝贵支持。加州大学的大卫豪斯勒博士 圣克鲁斯。他的团队将在高通量测序比对方面提供广泛的专业知识， 数据和开发广泛使用的基于网络的基因组数据浏览器。

Biomedical research has become a data intensive field. Breakthrough technologies such as microarrays are now more robust and affordable than ever, while others introduced more recently such as high throughput sequencing are following the same path. Flow cytometry, multiplex protein assays, protein arrays, which are some of the other platforms we are proposing to employ to monitor responses to vaccination will add to the large volume of data generated in the context of this research program. The role of the data mining core is to support the studies carried out by the center by providing access to a facility capable of processing and analyzing large volumes of immune profiling data, and the capacity to innovate and develop novel analysis tools and approaches. The foundation of the work carried out by the data mining core will be laid out by a solid data management infrastructure. Furthermore, the core will unify analytic strategies across projects by offering standardized data mining and biostatistics analysis pipelines. These will be used for instance by our bioinformatics team for the processing of the large volumes of microarray data generated by each of the research projects. In addition, our biostatistics team on the basis of feasibility data presented in this application has designed analytic strategies that will be used to interrogate immune profiling data. The data mining core will also provide access to cutting-edge bioinformatics analysis tools and strategies in order to meet the unique set of challenges posed by the multi-dimensional molecular profiling approaches that this program will apply to the study of responses to vaccines. For this we will rely upon the bioinformatics team at the BIIR, which has demonstrated its capacity for innovation, as illustrated by its publication record. Furthermore, we will also count ofthe invaluable support of our collaborator. Dr. David Haussler, at UC Santa Cruz. His team will lends its extensive expertise with the alignment of high throughput sequencing data and the development of widely used web-based genomic data browsers.