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

COBRE：UID：项目 4：微生物群落分析的统计方法

• 批准号: 8167455
• 负责人: Zaid Abdo
• 金额: $ 11.92万
• 依托单位: UNIVERSITY OF IDAHO
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-02-01 至 2011-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8167455
• 关键词: Achievement; Age; Classification; Communities; Computer Retrieval of Information on Scientific Projects Database; Computer software; Data; Decision Support Systems; Diagnostic; Diagnostics Research; Disease; Ecosystem; Equilibrium; Ethnic Origin; Funding; Gender; Goals; Grant; Health; Human; Institution; Knowledge; Medical; Metadata; Methods; Modeling; Monte Carlo Method; Nature; Probability Theory; Recording of previous events; Research; Research Personnel; Resources; Sampling; Simulate; Source; Staging; Statistical Methods; Structure; Symptoms; United States National Institutes of Health; Validation; base; disease diagnosis; microbial; microbial community; pathogen

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.

该子项目是利用该技术的众多研究子项目之一 资源由 NIH/NCRR 资助的中心拨款提供。子项目和 研究者 (PI) 可能已从 NIH 的另一个来源获得主要资金， 因此可以在其他 CRISP 条目中表示。列出的机构是 中心，不一定是研究者的机构。 疾病的发生不仅是由于有害病原体的孤立作用，也是由于人类微生物生态系统平衡的破坏。这一新兴知识需要对当前的诊断方法进行修订，以纳入有关这些生态系统“正常”状态的信息以及偏离该状态可能导致疾病的性质的信息。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来区分正常和异常（与疾病症状相关）微生物群落，同时考虑元数据（例如性别、年龄、种族和健康史）。具体来说，我们的目标是：1）开发基于模型的聚类方法来准确区分和表征不同的微生物群落群体；2）开发基于模型的分类方法来正确有效地将新采样的微生物群落分类为预先存在的、特征良好的群体。我们将分三个阶段实现这些具体目标：建模、实施和验证。建模将涉及使用概率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关联中使用蒙特卡罗方法。将使用真实和模拟数据对所提出的方法的准确性进行验证。我们目标的实现将构成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，我们将开发软件，供医学界用于研究和诊断目的。