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将病原体基因分型和患者流行病学数据相结合的方法，可用于控制、了解和跟踪传染病。由于全球的迫切需求和国家结核病基因分型计划的独特数据可获得性，这项工作的重点是为结核病的病原体结核分枝杆菌复合体(MTC)建立大量的国际患者流行病学和菌株数据的模型。具体地说，该项目解决了以下问题：鉴于MTC DNA指纹和结核病患者数据正在国内和国际上积累，利用机器学习识别捕捉MTC基因家族和结核病流行病学的隐藏群体，并利用这些隐藏群体在城市、州、国家和国际各级解决结核病控制、理解、预防和治疗方面的问题。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患者分离基因类型和相关患者信息的大型数据库。第二个目标是使用受背景知识约束的图形模型，基于多种基因分型方法识别MTC菌株家族。第三个目标是使用概率图形模型和确定性多向张量分析方法的组合来识别结核病患者人口统计学和MTC基因型别中隐藏的宿主病原体群体，旨在捕捉结核病的时间动态。第四个目标是回答结核病专家提出的公共卫生问题，将这些问题转化为适用于隐藏群体的可量化指标。隐藏组模型和指标将嵌入分析方法，然后由结核病专家进行评估。建议的模型和分析方法将捕获和共享嵌入大型结核病患者和MTC基因分型数据库的知识，而不一定共享实际数据。

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.