TRD3 NMRbox: Bayesian Analytics

TRD3 NMRbox：贝叶斯分析

• 批准号: 9276080
• 负责人: JEFFREY C HOCH
• 金额: $ 26.99万
• 依托单位: UNIVERSITY OF CONNECTICUT SCH OF MED/DNT
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/9276080
• 关键词: Address; Advanced Development; Algorithms; Area; Automation; Bayesian Analysis; Bayesian Method; Biological; Biomolecular Nuclear Magnetic Resonance; Chemicals; Communities; Computer software; Computing Methodologies; Data; Data Analyses; Data Analytics; Data Collection; Databases; Development; Ensure; Environment; Future; Investigation; Knowledge; Measures; Methodology; NMR Spectroscopy; Network-based; Plug-in; Procedures; Process; Proteins; RNA; Reliability of Results; Reproducibility; Research; Side; Software Tools; Solid; Speed; Statistical Models; Structure; System; Technology; Testing; Time; base; computerized data processing; computerized tools; computing resources; data format; data modeling; design; disease diagnosis; drug discovery; experience; experimental study; flexibility; improved functioning; innovation; innovative technologies; knowledge base; mathematical methods; metabolic abnormality assessment; metabolomics; novel; open source; operation; portability; structural biology; tool; virtual

Abstract: TRD 3. Biological applications of NMR have expanded to include broad areas of investigation in structural biology, metabolic studies, disease diagnosis, and drug discovery. NMR offers detailed information, but the interpretation involves a multistep and intricate process that uses a range of mathematical and computational methods. Powerful software packages have been developed to facilitate data collection, analysis, and interpretation, but significant challenges remain. The aim of this TRD is to deliver a robust and reliable statistical framework, based on Bayesian principles, in order to advance the state of NMR data analytics, simplify the future development of advanced NMR applications that use statistical principles, and provide significant new applications of this framework in the context of NMR. Central components of this TRD include the development of a Bayesian inference engine and a set of programming interfaces, the redeployment of some legacy applications on the new platform, provide templates for development of applications by other research teams, and integration of the technology within the platform being developed by the Center. The implementation of this TRD will benefit from key advantages offered by the NMRbox platform. The resulting Bayesian inference technology will see significantly lowered barriers to use, substantially reduced time for development of new applications, broader accessibility, and higher visibility. 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 help ensure the reproducibility bio-NMR studies.

摘要：TRD 3。NMR 的生物学应用已扩展到包括以下广泛的研究领域： 结构生物学、代谢研究、疾病诊断和药物发现。 NMR 提供详细信息， 但解释涉及一个多步骤且复杂的过程，其中使用了一系列数学和 计算方法。已开发出功能强大的软件包以方便数据收集， 分析和解释，但仍然存在重大挑战。该 TRD 的目标是提供一个强大且 基于贝叶斯原理的可靠统计框架，以推进 NMR 数据的状态 分析，简化使用统计原理的高级 NMR 应用程序的未来开发，以及 为该框架在 NMR 领域提供了重要的新应用。该 TRD 的核心组件 包括开发贝叶斯推理引擎和一组编程接口， 在新平台上重新部署一些遗留应用程序，提供开发模板 其他研究团队的应用，以及将技术集成到正在开发的平台中 中心。该 TRD 的实施将受益于 NMRbox 平台提供的关键优势。 由此产生的贝叶斯推理技术将显着降低使用门槛，大幅减少 开发新应用程序、更广泛的可访问性和更高的可见性的时间。通过促进 这项先进技术的部署、利用、互操作和持久性，拟议的 TRD 将 推进 NMR 在生物医学领域各种具有挑战性的应用中的应用，并帮助确保 生物核磁共振研究的可重复性。