Data Mining

数据挖掘

• 批准号: 8307083
• 负责人: Damien Chaussabel
• 金额: $ 26.73万
• 依托单位: BAYLOR RESEARCH INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-07-05 至 2015-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8307083
• 关键词: Bioinformatics; Biological Assay; Biomedical Research; Biometry; California; Core Facility; Data; Development; Flow Cytometry; Fostering; Foundations; Genomics; Human; Immune; Institution; Instruction; Meta-Analysis; Molecular Profiling; Monitor; Online Systems; Output; Play; Process; Protein Array Analysis; Proteins; Proteomics; Publications; Research; Research Infrastructure; Research Project Grants; Role; Running; Software Engineering; Solid; Solutions; Systems Analysis; Technology; Universities; Vaccination; Vaccines; Work; base; data management; data mining; design; innovation; meetings; novel; programs; research study; response; tool

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.

生物医学研究已成为一个数据密集型领域。像微阵列这样的突破性技术