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.

这个子项目是众多研究子项目之一