Statistical Data Mining of Time-Dependent Data with Applications in Geoscience and Biology

时变数据的统计数据挖掘及其在地球科学和生物学中的应用

• 批准号: 0431085
• 负责人: Padhraic Smyth
• 金额: --
• 依托单位: University of California-Irvine
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-09-01 至 2009-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0431085&HistoricalAwards=false
• 关键词: Statistical; Data; Mining; Time; Dependent

This project is developing novel statistical data mining methods that will help scientists to explore and understand large observational data sets. A specific focus of this research is on developing algorithms that can extract, from large data sets, models of dynamic behavior of objects over time, with an emphasis on selected data-driven problems in biology and geoscience. Examples include automated discovery of genetic regulatory mechanisms from expression measurements over time, and clustering and prediction of cyclone behavior over time. Statistical learning principles are being used to guide algorithm development and to produce publicly-available software tools. An educational component of this project is leading to an increased awareness among students of the important role of computer science and statistics in data-driven science.The results from this project have the potential for significant and broad impact in the primary focus areas of geoscience and biology, as well as in other scientific and engineering areas involving large observational data sets from dynamic processes. In the geosciences, the new algorithms can yield improved modeling and prediction of extra-tropical and tropical cyclones, reducing the socio-economic risks associated with cyclonic events and potentially provide valuable clues about possible climate change. In the biosciences, improved understanding of gene regulatory mechanisms (obtained via new network discovery algorithms) can provide the basis for significant advances in systems biology and medicine, such as the identification of the regulatory mechanisms for cancer-related genes and resultant development of gene-specific medical treatments.

该项目正在开发新的统计数据挖掘方法，帮助科学家探索和理解大型观测数据集。这项研究的一个具体重点是开发算法，可以提取，从大型数据集，模型的动态行为的对象随着时间的推移，重点是选定的数据驱动的问题，在生物学和地球科学。例子包括随着时间的推移从表达测量自动发现遗传调节机制，以及随着时间的推移聚类和预测气旋行为。统计学习原理正被用来指导算法开发和制作公开可用的软件工具。该项目的一个教育部分正在提高学生对计算机科学和统计在数据驱动的科学中的重要作用的认识，该项目的成果有可能对地球科学和生物学的主要重点领域以及涉及动态过程的大型观测数据集的其他科学和工程领域产生重大和广泛的影响。在地球科学方面，新算法可以改进热带和热带气旋的建模和预测，减少与气旋事件相关的社会经济风险，并可能提供有关可能的气候变化的有价值的线索。在生物科学中，对基因调控机制的更好理解（通过新的网络发现算法获得）可以为系统生物学和医学的重大进展提供基础，例如识别癌症相关基因的调控机制以及由此产生的基因特异性医学治疗的发展。