Discovering hidden groups across tuberculosis patient and pathogen genotype data

描述（由申请人提供）： 该项目的主要目标是开发出将联合收割机病原体基因分型和患者流行病学数据相结合的方法，这些方法可用于控制、理解和跟踪传染病。这项工作的重点是建立大型国际收集的结核分枝杆菌复合群（MTC），结核病（TB）的病原体的患者流行病学和菌株数据的模型，因为迫切的全球需求和独特的数据可用性，由于国家TB基因分型计划。具体而言，该项目解决了以下问题：鉴于MTC DNA指纹图谱和结核病患者数据正在国内和国际上积累，使用机器学习识别捕获MTC遗传家族和结核病流行病学的隐藏组，并使用这些隐藏组来解决城市，州，国家和国际层面的结核病控制，理解，预防和治疗问题。为了实现这一目标，我们确定了几个目标。第一个目标是收集和合并来自纽约市、纽约州、美国和世界其他地区的MTC患者分离基因型以及相关患者信息的大型数据库。第二个目的是确定MTC菌株家族的基础上，多基因型的方法，使用图形模型约束，以反映背景知识。第三个目标是使用概率图形模型和确定性多路张量分析方法的组合来识别TB患者人口统计学和MTC基因型内隐藏的宿主-病原体组，所述确定性多路张量分析方法被设计用于捕获TB的时间动态。第四个目标是通过将结核病专家提出的问题转化为适用于隐藏群体的可量化指标来回答这些问题。隐藏组模型和度量将嵌入到分析方法中，然后由结核病专家进行评估。所提出的模型和分析方法将捕获和共享嵌入在大型TB患者和MTC基因分型数据库中的知识，而不必共享实际数据。

DESCRIPTION (provided by applicant): The principal objective of this project is to develop methods that combine pathogen genotyping and patient epidemiology data that can be used in the control, understanding, and tracking of infectious diseases. This work focuses on the modeling of large international collections of patient epidemiology and strain data for the Mycobacterium tuberculosis complex (MTC), the causative agent of tuberculosis disease (TB), because of the urgent global need and the unique data availability due to the National TB genotyping program. Specifically, the project addresses the following problem: given MTC DNA fingerprinting and TB patient data being accumulated nationally and internationally, identify hidden groups capturing MTC genetic families and TB epidemiology using machine learning, and use these hidden groups to address problems in the control, understanding, prevention, and treatment of tuberculosis at city, state, national, and international levels. To address this objective, we identify several aims. The first aim is to gather and merge large databases of MTC patient-isolate genotypes as well as associated patient information from the New York City, New York State, United States, and the rest of the world. The second aim is to identify MTC strain families based on multiple genotype methods using graphical models constrained to reflect background knowledge. The third aim is to identify hidden host-pathogen groups within TB patient demographics and MTC genotypes using a combination of probabilistic graphical models and deterministic multi-way tensor analysis methods designed to capture the temporal dynamics of TB. The fourth aim answers public health questions posed by TB experts by transforming the questions into quantifiable metrics applied to the hidden groups. The hidden group models and metrics will be embedded in analysis methods, and then evaluated by TB experts. The proposed models and analysis methods will capture and share knowledge embedded in large TB patient and MTC genotyping databases without necessarily sharing the actual data.