ATD: Statistical Methods for Threat Detection

ATD：威胁检测的统计方法

• 批准号: 1043204
• 负责人: David Siegmund
• 金额: $ 71.1万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-10-01 至 2015-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1043204&HistoricalAwards=false
• 关键词: ATD; Statistical; Methods; Threat; Detection

Successful statistical analysis of the massive amounts of data available today can lead to successful, early threat detection. This proposal consists of two parts. The first part focuses on the detection of mutations in pathogen samples. Many emerging health threats are due to new mutations in evolving pathogen populations, which can now be profiled using massively parallel sequencing experiments. The investigators work with Dr. Hanlee Ji's laboratory in the Stanford Genome Technology Center, whose deep sequencing platform allows the detection of low prevalence mutations in pathogen samples. This problem was previously treated mainly from an algorithmic perspective, lacking statistical models for error estimates. The investigators propose methods for analysis of single nucleotide changes and general structural variants, and consider the analysis of single samples, the simultaneous analysis of multiple samples, and the comparison of matched samples. The second part of the proposal considers threat detection in a more general framework: detection of changes from background condition in one or more parallel streams of data. Examples are cyber-attacks on computer networks, introduction of belligerent agents (e.g. landmines, aircraft) into previously quiescent environments, appearance of noxious chemicals, genetic modifications of viruses or bacteria, etc. The main contribution is a general conceptual framework for integrating data from a large number of distributed sources, when the signal of interest may be present in only a small fraction of the sources. This proposal motivates theoretical developments in the areas of change-point detection, mixture estimation, empirical Bayes estimation, and false discovery rate control. Successful statistical analysis of the massive amounts of data collected in modern scientific and technological activities can lead to successful, early threat detection. This proposal consists of two parts. The first part focuses on the detection of mutations in pathogen samples. Many emerging health threats are due to new mutations in evolving pathogen populations, which can now be profiled using next generation sequencing experiments. The accurate detection of new mutations is important, because they may confer survival advantage to the virus that carries it. Currently, this problem has been treated mainly from an algorithmic perspective, lacking statistical models for error estimates. The methods developed in this proposal will bridge this gap. The second part of the proposal considers threat detection in a more general framework: detection of changes from background condition in one or more parallel streams of data. Examples include cyber-attacks on computer networks, introduction of belligerent agents (e.g. landmines, aircraft) into previously quiescent environments, appearance of noxious chemicals, genetic modifications of viruses or bacteria, etc. The main contribution is a general conceptual framework for integrating data from a potentially large number of distributed sources, when the signal of interest may be present in only a small fraction of the sources.

对当今可用的大量数据进行成功的统计分析可以实现成功的早期威胁检测。这项建议包括两个部分。第一部分着重于病原体样本中突变的检测。许多新出现的健康威胁是由于不断演变的病原体群体中的新突变，现在可以使用大规模并行测序实验进行分析。 研究人员与斯坦福大学基因组技术中心的Hanlee Ji博士实验室合作，该实验室的深度测序平台可以检测病原体样本中的低流行率突变。这个问题以前主要从算法的角度来处理，缺乏误差估计的统计模型。研究人员提出了分析单核苷酸变化和一般结构变异的方法，并考虑了单个样品的分析，多个样品的同时分析以及匹配样品的比较。该提案的第二部分在更一般的框架中考虑威胁检测：检测一个或多个并行数据流中背景条件的变化。例子是计算机网络上的网络攻击，引入交战代理（如地雷，飞机）到以前静止的环境中，有毒化学品的外观，病毒或细菌的遗传修饰，等的主要贡献是一个一般的概念框架整合数据从大量的分布式源，当感兴趣的信号可能只存在于一小部分的来源。这一建议激励理论的发展领域的变化点检测，混合估计，经验贝叶斯估计，和错误发现率控制。对现代科学技术活动中收集的大量数据进行成功的统计分析，可以成功地进行早期威胁检测。这项建议包括两个部分。第一部分着重于病原体样本中突变的检测。许多新出现的健康威胁是由于不断演变的病原体群体中的新突变，现在可以使用下一代测序实验进行分析。新突变的准确检测非常重要，因为它们可能赋予携带它的病毒生存优势。目前，这个问题主要从算法的角度来处理，缺乏用于误差估计的统计模型。本提案中提出的方法将弥补这一差距。该提案的第二部分在更一般的框架中考虑威胁检测：检测一个或多个并行数据流中背景条件的变化。例子包括对计算机网络的网络攻击，引入交战代理（如地雷，飞机）到以前静止的环境中，有毒化学品的外观，病毒或细菌的遗传修饰等的主要贡献是一个一般的概念框架整合数据从一个潜在的大量的分布式源，当感兴趣的信号可能只存在于一小部分的来源。