NMRbox: Bayesian Analytics

NMRbox：贝叶斯分析

• 批准号: 10406894
• 负责人: HAMID R EGHBALNIA
• 金额: $ 24.4万
• 依托单位: UNIVERSITY OF CONNECTICUT SCH OF MED/DNT
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10406894
• 关键词: Address; Advanced Development; Area; Automation; Bayes Theorem; Bayesian Analysis; Bayesian Method; Biological; Biomolecular Nuclear Magnetic Resonance; Cells; Collaborations; Communities; Complement; Computer Analysis; Computer software; Computing Methodologies; Data; Data Analyses; Data Analytics; Deposition; Development; Diagnostic; Ecosystem; Ensure; Evaluation; Experimental Models; Future; Goals; Health Status; Hour; Intelligence; Investigation; Knowledge; Lead; Libraries; Location; Mass Spectrum Analysis; Metabolic; Modeling; Molecular; Molecular Profiling; NMR Spectroscopy; Probability; Procedures; Process; Proteins; Relaxation; Reliability of Results; Reproducibility; Research Personnel; Resource Development; Resources; Sampling; Science; Shapes; Source; Statistical Models; Technology; Translations; Validation; Visual; Visualization software; Work; analytical method; base; computational platform; computer framework; computerized data processing; data exchange; data format; data modeling; design; drug discovery; flexibility; heterogenous data; improved; instrument; mathematical methods; metabolomics; preservation; signature molecule; small molecule; support tools; tool; virtual machine

Abstract: TRD 3. Nuclear magnetic resonance spectroscopy (NMR) is a powerful analytical instrument with numerous applications in biomedicine. Some examples of high-impact investigation are studies using metabolite profiles as markers of health status, evaluation of small molecules for biological activity as a drug, and discovery of new metabolic compounds. NMR offers detailed information, but the interpretation of data involves a multistep process that often involves using data from other analytical instruments. The use of a range of sophisticated mathematical and computational methods is required, and this is often a major bottleneck. The aim of this TRD is to deliver enhanced functionality for robust and reliable statistical framework, based on Bayesian principles. This project will advance the state of NMR data analytics and simplifies the future development of advanced NMR applications. Central components of this TRD include a Bayesian inference engine, tools for validation of experimental models and results, a set of programming interfaces, and a library of templates for facilitating the identification of metabolite and small molecule signatures in biological mixtures. The implementation of this TRD will benefit from key advantages offered by the NMRbox platform. By facilitating the deployment, utilization, interoperation, and persistence of this advanced technology, the proposed TRD will advance the application of NMR for a wide range of challenging applications in biomedicine and helps ensure the reproducibility bio-NMR studies.

摘要：TRD3.核磁共振波谱是一种强大的分析技术 在生物医学中有许多应用的仪器。一些高影响力的例子 调查是使用代谢物概况作为健康状况的标志，评估 作为药物的生物活性的小分子，以及新代谢化合物的发现。 核磁共振提供了详细的信息，但数据的解释涉及一个多步骤的过程 通常涉及使用来自其他分析仪器的数据。使用了一系列复杂的 需要数学和计算方法，而这往往是一个主要的瓶颈。这个 这一TRD的目的是为稳健和可靠的统计框架提供增强的功能， 基于贝叶斯原理。该项目将推动核磁共振数据分析和 简化了高级核磁共振应用的未来开发。它的核心组件 TRD包括贝叶斯推理引擎、用于验证实验模型工具和 结果、一组编程接口和模板库，以促进 生物混合物中代谢物和小分子特征的鉴定。这个 这一TRD的实施将受益于NMRbox平台提供的主要优势。 通过促进部署、利用、互操作和持久保存此高级 技术方面，拟议的TRD将推动核磁共振在广泛领域的应用 在生物医学中具有挑战性的应用，并有助于确保生物核磁共振研究的重复性。