ATD: Statistical Methods for Threat Detection

对当今可用的海量数据进行成功的统计分析可以成功地及早检测到威胁。这项建议由两部分组成。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种群中的新突变，现在可以使用大规模平行测序实验来分析这些突变。研究人员与纪汉礼博士在斯坦福基因组技术中心的实验室合作，该实验室的深度测序平台允许检测病原体样本中的低流行突变。这个问题以前主要是从算法的角度来处理的，缺乏用于误差估计的统计模型。研究人员提出了单核苷酸变化和一般结构变异的分析方法，并考虑了单个样本的分析、多个样本的同时分析和匹配样本的比较。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。例如，对计算机网络的网络攻击、在以前静止的环境中引入交战剂(如地雷、飞机)、出现有毒化学品、对病毒或细菌进行基因改造等。主要贡献是在感兴趣的信号可能只出现在一小部分来源时，为整合来自大量分散来源的数据提供了一个总体概念框架。这一建议推动了变点检测、混合估计、经验贝叶斯估计和错误发现率控制等领域的理论发展。对现代科学和技术活动中收集的大量数据进行成功的统计分析，可以成功地及早发现威胁。这项建议由两部分组成。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来分析这些突变。准确检测新的突变很重要，因为它们可能会给携带新突变的病毒带来生存优势。目前，这一问题主要是从算法的角度来处理的，缺乏用于误差估计的统计模型。本提案中开发的方法将弥合这一差距。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、将交战剂(如地雷、飞机)引入以前静止的环境、出现有毒化学品、对病毒或细菌进行基因改造等。主要贡献是当感兴趣的信号可能只在一小部分来源中出现时，用于整合来自潜在大量分布式来源的数据的一般概念框架。

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.