NSF Conference in the Mathematical Sciences on Data Mining and Bioinformatics; January 8-10, 2004; Gainesville, FL

NSF 数据挖掘和生物信息学数学科学会议；

• 批准号: 0337163
• 负责人: George Casella
• 金额: $ 1.75万
• 依托单位: University of Florida
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-08-15 至 2004-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0337163&HistoricalAwards=false
• 关键词: NSF; Conference; Mathematical; Sciences; Data

DMS-0337163 PI: George CasellaABSTRACTNSF Conference in the Mathematical Sciences on Data Mining and Bioinformatics at the University of Florida, January 8-10, 2004There is a large demand for statistical tools to help us analyze and understand massive amounts of data. Traditional statistical approaches often fail to cope with the underlying complexity of such datasets. Some of the potential statistical issues are model selection, including algorithms to search through model spaces, robustness, data quality and sampling, multiplicity issues, inference in high-dimensional, small sample ("large p small n") problems, appropriate scaling of data, and inference based on complex datasets from medical images, microarrays or environmental monitoring. Biological questions inherent in such data include determining the three dimensional structure of proteins based on DNA sequences and determining the differential expression levels of thousands of genes from data collected on microarrays. Data mining (DM) is the generic term that encompasses such methods for massive datasets. Data mining on biological and genomic data is often called Bioinformatics (Bio). The topic of Data Mining and Bioinformatics is ideal for a NSF regional conference. It appeals to a wide spectrum of researchers with diverse statistical interests from those interested in internet trafficking to fraud detection to microarrays to protein structure. The challenge of drawing inferences based on these massive datasets will appeal to those interested in theoretical and methodological statistics. Finally, the excitement of accepting the fine challenge of analyzing unorthodox data where existing statistical methodology is not satisfactory will undoubtedly fascinate researchers concerned with applications of statistics.It is hoped that this conference will provide an assessment of the current state of the art in the workings and use of DM/Bio, bring up open problems, and foster collaboration among research workers in academia, industry and government in an effort to provide solutions to these problems and answer questions of great importance to both science and society. We expect the conference to generate interest in this topic among researchers nationwide (particularly young researchers), among faculty and graduate students at the University of Florida and neighboring universities, and promote interactions between junior and senior researchers.

DMS-0337163 主要研究者：乔治CasellaABSTRACTNSF会议在数学科学的数据挖掘和生物信息学在佛罗里达大学，2004年1月8日至10日有一个统计工具，以帮助我们分析和理解大量的数据有很大的需求。 传统的统计方法往往无法科普这些数据集的潜在复杂性。一些潜在的统计问题是模型选择，包括搜索模型空间的算法，鲁棒性，数据质量和采样，多重性问题，高维小样本（“大p小n”）问题的推断，数据的适当缩放，以及基于医学图像，微阵列或环境监测的复杂数据集的推断。 这些数据中固有的生物学问题包括基于DNA序列确定蛋白质的三维结构，以及从微阵列上收集的数据确定数千个基因的差异表达水平。 数据挖掘（DM）是一个通用术语，它包含了用于大规模数据集的此类方法。 对生物和基因组数据的数据挖掘通常被称为生物信息学（Bio）。 数据挖掘和生物信息学的主题是NSF区域会议的理想选择。它吸引了广泛的研究人员，他们对互联网交易、欺诈检测、微阵列和蛋白质结构感兴趣。 根据这些大规模数据集进行推断的挑战将吸引那些对理论和方法统计感兴趣的人。 最后，在现有的统计方法不令人满意的情况下，接受分析非正统数据的挑战无疑会使关注统计应用的研究人员着迷。希望这次会议将提供对DM/Bio工作和使用的当前技术状态的评估，提出开放问题，并促进学术界研究人员之间的合作，工业界和政府努力为这些问题提供解决方案，并回答对科学和社会都非常重要的问题。 我们希望这次会议能引起全国研究人员（特别是年轻研究人员），佛罗里达大学和邻近大学的教师和研究生对这一主题的兴趣，并促进初级和高级研究人员之间的互动。