COBRE: UID: PROJ 4: STATISTICAL METHODS FOR THE ANALYSIS OF MICROBIAL COMMUNITY

这个子项目是许多研究子项目中利用 资源由NIH/NCRR资助的中心拨款提供。子项目和 调查员(PI)可能从NIH的另一个来源获得了主要资金， 并因此可以在其他清晰的条目中表示。列出的机构是 该中心不一定是调查人员的机构。 疾病的发生不仅是由于单独行动的有害病原体，而且还因为破坏了人类-微生物生态系统的平衡。这一新兴知识要求对目前的诊断方法进行修订，以纳入有关这些生态系统的“正常”状态以及可能导致疾病的偏离这种状态的性质的信息。它还需要向医学界提供这些修订后的方法，以方便诊断疾病。该项目的目标是开发计算方法，以区分正常和异常(与疾病症状有关)微生物群落，并考虑到元数据(如性别、年龄、种族和健康史)。具体地说，我们的目标是：1)开发基于模型的聚类方法，以准确地区分和表征不同的微生物群落；2)开发基于模型的分类方法，以正确和高效地将新采样的微生物群落分类为预先存在的、特征良好的群落。我们将通过建模、实现和验证三个阶段来实现这些具体目标。建模将涉及使用概率理论和贝叶斯框架来捕获数据中可用的信息。实施将涉及在模型参数估计和微生物群落-群体关联中使用蒙特卡罗方法。将使用真实数据和模拟数据来验证所提出的方法的准确性。我们的目标的实现将形成一个决策支持系统的基础，以帮助临床医生识别与正常微生物群落结构的偏差。为此，我们将制作供医学界用于研究和诊断目的的软件。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Diseases occur not only due to harmful pathogens that act in isolation but also due to disruption of the balance of the human-microbial ecosystem. This emerging knowledge requires a revision of the current diagnostic approaches to incorporate information about the "normal" state of these ecosystems and the nature of deviation from this state that can result in disease. It also requires making these revised approaches readily available to the medical community to facilitate diagnosing diseases. The goal for this project is to develop computational approaches to differentiate between normal and abnormal (associated with disease symptoms) microbial communities, taking metadata (such as gender, age, ethnicity, and health history) into consideration. Specifically, we aim to: 1) develop model-based clustering methods to accurately distinguish and characterize different microbial community groups and 2) develop model-based classification methods to correctly and efficiently classify newly sampled microbial communities to pre-existing, well characterized groups. We will attain these specific aims in three stages: modeling, implementation and validation. Modeling will involve the use of probability theory and a Bayesian framework to capture the information available in the data. Implementation will involve the use of Monte Carlo methods in model-parameter estimation and microbial community-group association. Validation of the accuracy of the proposed methods will be performed using real and simulated data. Achievement of our Aims will form the basis of a decision-support system to assist clinicians in identifying deviations from normal microbial community structures. For this purpose we will produce software that will be made available to the medical community for research and diagnostic purposes.