NMRbox: Bayesian Analytics

NMRbox：贝叶斯分析

基本信息

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
项目类别：
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

项目摘要

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：TRD 3。核磁共振光谱（NMR）是一种强大的分析在生物医学中具有众多应用的仪器。一些高影响的例子调查是使用代谢物概况作为健康状况标志的研究，评估用于生物活性的小分子作为药物，并发现新的代谢化合物。 NMR提供了详细的信息，但是数据的解释涉及多步骤的过程通常涉及使用其他分析仪器的数据。使用一系列精致的需要数学和计算方法，这通常是主要的瓶颈。这该TRD的目的是为可靠和可靠的统计框架提供增强的功能，基于贝叶斯原则。该项目将推进NMR数据分析状态和简化了高级NMR应用程序的未来开发。此的中心组成部分 TRD包括贝叶斯推理引擎，用于验证实验模型的工具和结果，一组编程接口和一个模板库，以促进在生物混合物中鉴定代谢物和小分子特征。这该TRD的实现将受益于NMRBox平台提供的关键优势。通过促进该高级的部署，利用，互操作和持久性技术，拟议的TRD将推动NMR的应用在生物医学中的挑战性应用，有助于确保可重复性生物-NMR研究。