Online learning platform: introducing clinicians and researchers to metabolomics

在线学习平台:向临床医生和研究人员介绍代谢组学

基本信息

  • 批准号:
    8416746
  • 负责人:
  • 金额:
    $ 5.57万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2012
  • 资助国家:
    美国
  • 起止时间:
    2012-09-18 至 2017-08-31
  • 项目状态:
    已结题

项目摘要

DESCRIPTION (provided by applicant): We propose the web-based delivery of a metabolomics training program for researchers and clinicians who are either not yet using metabolomic approaches or are still new to the field. We will develop a comprehensive set of learning modules helping clinicians and researchers to read about metabolomic research and become interested in incorporating metabolomic approaches into their own work. Each program module will cover carefully selected topics, allowing learners to improve their knowledge with modest time investments required. The program will provide both foundational concepts of metabolomics and typical applications for research and clinical uses. The materials will translate the most current knowledge and practical uses into an instructionally sound and user-friendly format. A limitation of conventional presentations and workshops is that they have to be scheduled for many users at the same time and usually require attendees to spend more time traveling than learning. This keeps many away who would greatly benefit and might have a better basis for using metabolomic tools in some direct or indirect way. The emphasis of the program modules will be on core principles and practical applications - how to translate the understanding of metabolomic principles into productive research and clinical benefits. Learners completing any program module will understand how to start reading about metabolomic research findings and planning the use of current metabolomic resources without being unduly encumbered by arcane details. Nonetheless, in our program, information about key metabolomic techniques and procedures will be available for review as needed. The modular and flexible format of our instructional units will optimize learning efficacy for clinicians and researchers by facilitating the brief and tightly focused coverage of critical metabolomic topics for the increasingly busy health professional. We will implement a software strategy that will tailor content delivery based on the learner's pre-existing knowledge. Built-in remediation components, based on concurrent monitoring of learning efficacy, will strengthen the efficiency of our instructional approach for all learners. We expect that efficient use of the learner's time will increase use and retention of this material. With NIH support, the material will be available to all US clinicians and researchers. PUBLIC HEALTH RELEVANCE: The proposed project will bring metabolomic science to researchers and clinicians, preparing them to decode reports of metabolomic research and start thinking about using current metabolomic technology and resources for their bench and clinical research. The requested support will help strengthen the use of metabolomic technologies and approaches by exposing trainees to key concepts and relevant practical exercises. Novel instructional technologies will make better use of the limited learning time for this important, bu often daunting subject.
描述(由申请人提供):我们建议为尚未使用代谢组学方法或仍然是该领域新手的研究人员和临床医生提供基于网络的代谢组学培训计划。我们将开发一套全面的学习模块,帮助临床医生和研究人员阅读有关代谢组学的研究,并对将代谢组学方法纳入他们自己的工作感兴趣。每个课程模块将涵盖精心挑选的主题,让学习者以适度的时间投资来提高他们的知识。该计划将提供代谢组学的基本概念和典型的研究和临床应用。这些材料将把最新的知识和实际用途转化为具有良好教学效果和便于使用的格式。传统的演示和研讨会的一个限制是,它们必须同时为许多用户安排,并且通常要求与会者花更多的时间旅行而不是学习。这让很多人远离了这些人,他们本可以从中受益,而且可能有更好的基础以某种直接或间接的方式使用代谢组学工具。课程模块的重点将放在核心原理和实际应用上-如何将对代谢组学原理的理解转化为生产性研究和临床效益。完成任何课程模块的学习者都将了解如何开始阅读有关代谢组学的研究成果,并计划使用当前的代谢组学资源,而不会被晦涩的细节所阻碍。尽管如此,在我们的项目中,有关关键代谢组学技术和程序的信息将根据需要进行审查。我们的教学单元的模块化和灵活的格式将优化临床医生和研究人员的学习效率,通过促进对日益繁忙的健康专业人员的关键代谢组学主题的简短和紧密集中的覆盖。我们将实施一种软件策略,根据学习者已有的知识定制内容交付。内置的补救组件,基于对学习效果的同步监测,将加强我们对所有学习者的教学方法的效率。我们期望有效地利用学习者的时间将增加这些材料的使用和记忆。在美国国立卫生研究院的支持下,这些材料将可用

项目成果

期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ monograph.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ sciAawards.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ conferencePapers.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ patent.updateTime }}

MARTIN KOHLMEIER其他文献

MARTIN KOHLMEIER的其他文献

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

{{ truncateString('MARTIN KOHLMEIER', 18)}}的其他基金

Online learning platform: introducing clinicians and researchers to metabolomics
在线学习平台:向临床医生和研究人员介绍代谢组学
  • 批准号:
    8717687
  • 财政年份:
    2012
  • 资助金额:
    $ 5.57万
  • 项目类别:

相似海外基金

CAREER: Blessing of Nonconvexity in Machine Learning - Landscape Analysis and Efficient Algorithms
职业:机器学习中非凸性的祝福 - 景观分析和高效算法
  • 批准号:
    2337776
  • 财政年份:
    2024
  • 资助金额:
    $ 5.57万
  • 项目类别:
    Continuing Grant
CAREER: From Dynamic Algorithms to Fast Optimization and Back
职业:从动态算法到快速优化并返回
  • 批准号:
    2338816
  • 财政年份:
    2024
  • 资助金额:
    $ 5.57万
  • 项目类别:
    Continuing Grant
CAREER: Structured Minimax Optimization: Theory, Algorithms, and Applications in Robust Learning
职业:结构化极小极大优化:稳健学习中的理论、算法和应用
  • 批准号:
    2338846
  • 财政年份:
    2024
  • 资助金额:
    $ 5.57万
  • 项目类别:
    Continuing Grant
CRII: SaTC: Reliable Hardware Architectures Against Side-Channel Attacks for Post-Quantum Cryptographic Algorithms
CRII:SaTC:针对后量子密码算法的侧通道攻击的可靠硬件架构
  • 批准号:
    2348261
  • 财政年份:
    2024
  • 资助金额:
    $ 5.57万
  • 项目类别:
    Standard Grant
CRII: AF: The Impact of Knowledge on the Performance of Distributed Algorithms
CRII:AF:知识对分布式算法性能的影响
  • 批准号:
    2348346
  • 财政年份:
    2024
  • 资助金额:
    $ 5.57万
  • 项目类别:
    Standard Grant
CRII: CSR: From Bloom Filters to Noise Reduction Streaming Algorithms
CRII:CSR:从布隆过滤器到降噪流算法
  • 批准号:
    2348457
  • 财政年份:
    2024
  • 资助金额:
    $ 5.57万
  • 项目类别:
    Standard Grant
EAGER: Search-Accelerated Markov Chain Monte Carlo Algorithms for Bayesian Neural Networks and Trillion-Dimensional Problems
EAGER:贝叶斯神经网络和万亿维问题的搜索加速马尔可夫链蒙特卡罗算法
  • 批准号:
    2404989
  • 财政年份:
    2024
  • 资助金额:
    $ 5.57万
  • 项目类别:
    Standard Grant
CAREER: Efficient Algorithms for Modern Computer Architecture
职业:现代计算机架构的高效算法
  • 批准号:
    2339310
  • 财政年份:
    2024
  • 资助金额:
    $ 5.57万
  • 项目类别:
    Continuing Grant
CAREER: Improving Real-world Performance of AI Biosignal Algorithms
职业:提高人工智能生物信号算法的实际性能
  • 批准号:
    2339669
  • 财政年份:
    2024
  • 资助金额:
    $ 5.57万
  • 项目类别:
    Continuing Grant
DMS-EPSRC: Asymptotic Analysis of Online Training Algorithms in Machine Learning: Recurrent, Graphical, and Deep Neural Networks
DMS-EPSRC:机器学习中在线训练算法的渐近分析:循环、图形和深度神经网络
  • 批准号:
    EP/Y029089/1
  • 财政年份:
    2024
  • 资助金额:
    $ 5.57万
  • 项目类别:
    Research Grant
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了