NMRbox: Bayesian Analytics

NMRbox：贝叶斯分析

• 批准号: 10652475
• 负责人: HAMID R EGHBALNIA
• 金额: $ 14.11万
• 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/10652475
• 关键词: Address; Advanced Development; Area; Automation; Bayes Theorem; Bayesian Analysis; Bayesian Method; Biological; Biomolecular Nuclear Magnetic Resonance; Cells; Collaborations; Communication; 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; Pharmaceutical Preparations; Probability; Procedures; Process; Proteins; Relaxation; Reliability of Results; Reproducibility; Research Personnel; Resource Development; Resources; Sampling; Science; Shapes; Source; Specific qualifier value; Statistical Models; Technology; Technology Assessment; Translations; Validation; Visual; Visualization software; Work; analytical method; community engagement; computational platform; computer framework; computerized data processing; data exchange; data format; data modeling; design; 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.

翻译后摘要：TRD 3。核磁共振光谱（NMR）是一种强有力的分析方法， 在生物医学中有许多应用的仪器。高影响力的一些例子 调查是使用代谢物谱作为健康状况标志物的研究， 小分子作为药物的生物活性，以及发现新的代谢化合物。 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。使用一系列复杂的 需要数学和计算方法，这往往是一个主要的瓶颈。的 该TRD的目的是为强大和可靠的统计框架提供增强的功能， 基于贝叶斯原理。该项目将推进NMR数据分析的发展， 简化了先进NMR应用的未来发展。核心组成部分， TRD包括一个贝叶斯推理引擎，用于验证实验模型的工具， 结果、一组编程接口和一个模板库， 生物混合物中代谢物和小分子特征鉴定。的 该TRD的实施将受益于NMRbox平台提供的关键优势。 通过促进这种高级的 技术，拟议的TRD将推进NMR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NMR研究的重现性。