Data Mining

生物医学研究已经成为一个数据密集型领域。像微阵列这样的突破性技术 现在比以往任何时候都更强大和经济实惠，而其他产品则是最近推出的，例如高吞吐量 测序也遵循着同样的道路。流式细胞术，多重蛋白质分析，蛋白质阵列，这些都是 我们建议使用的其他一些监测疫苗接种反应的平台将增加 在这项研究计划的背景下产生的大量数据。数据挖掘核心的作用是 为中心开展的研究提供支持，方法是允许进入能够处理和 分析大量的免疫图谱数据，以及创新和开发新分析的能力 工具和方法。数据挖掘核心开展的工作的基础将由一个 坚实的数据管理基础设施。此外，核心将通过以下方式统一项目间的分析策略 提供标准化的数据挖掘和生物统计分析管道。例如，这些将由我们的 生物信息学团队，用于处理由每一家公司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这份报告中提供的可行性数据 应用程序设计了分析策略，将用于询问免疫图谱数据。数据 挖掘核心还将提供获得尖端生物信息学分析工具和战略的途径，以便 应对多维分子分析方法带来的一系列独特挑战 该计划将应用于疫苗反应的研究。为此，我们将依靠生物信息学团队 BIIR已经展示了其创新能力，其出版记录说明了这一点。 此外，我们还将感谢我们合作者的无价支持。加州大学的大卫·豪斯勒博士 圣克鲁斯。他的团队将利用其在高通量测序方面的广泛专业知识 数据和广泛使用的基于网络的基因组数据浏览器的开发。

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.