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A1: A Multi-Scale Open Knowledge Network for Biomedicine

A1: A Multi-Scale Open Knowledge Network for Biomedicine
A1:生物医学多尺度开放知识网络
批准号:
2033569
负责人:
Sergio Baranzini
金额:
$500.0万
依托单位国家:
美国
项目类别:
Cooperative Agreement
财政年份:
2020
资助国家:
美国
项目状态:
已结题
起止时间:
2020-09-01 至 2023-08-31

项目摘要

项目成果

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中文摘要
翻译
NSF Convergence Accelerator支持以使用为灵感的、基于团队的、多学科的努力,以应对国家重要性的挑战,并将在不久的将来为社会提供有价值的成果。该项目将创建一个称为知识网络的数据组织,使医生、研究人员、制药行业和公民科学家能够更有效地理解和探索生物医学。它将以一种允许提出重要新问题的方式连接大量数据,帮助发现生物过程的根源,确定疾病的治疗方法,识别可能与以前未探索的条件相关的药物等等。该平台将支持第三方创建的生物医学应用程序。这些工具的采用有可能产生重大的社会影响:降低医疗成本,健康差异和加速治疗,最终提高每个美国人的生活质量。美国人的医疗成本几乎占美国GDP的五分之一。健康差距、重大公共卫生问题、药物发现复杂性和总体成本继续大幅增长。人类健康的机制是如此复杂,以至于人类大脑无法整合与治疗患者或发现疗法相关的不断增长的可用知识。这阻碍了新知识的产生,特别是在生物医学科学及其对人类健康的影响方面。该项目的目标是建立一个生物医学开放知识网络(OKN),将数十亿个生物医学概念整合到一个知识引擎中,使医生、药物开发人员、研究人员和公民科学家能够快速、廉价地为生物医学问题提供有生物学意义的答案。该OKN将纳入生物医学概念之间的数十亿个事实关系,使专家通才能够全面探索生物医学。该项目支持的团队是开创生物医学知识网络范式的团队的一部分。这项工作汇集了在搜索工具(包括谷歌),图论(从劳伦斯利弗莫尔国家实验室),并与国家推进转化科学中心的生物医学数据翻译(美国国立卫生研究院的一部分),以及与其他学术非营利性机构(系统生物学研究所,印第安纳州大学,加州大学圣地亚哥分校和斯坦福大学的合作专业知识的合作伙伴。这项雄心勃勃的努力代表了融合研究,包括生物医学和数据科学各个方面的专业知识,整合了医生,研究人员,认识论专家,数据库专家,计算机科学家和统计学家。在融合加速器计划的第一阶段,该团队开发并提供了一个功能齐全的生物医学知识网络,可扩展的精准医学知识引擎(SPOKE,spoke.rbvi.ucsf.edu)。这一成功支持了该团队在第二阶段项目中产生可交付成果的可能性,这将对社会产生积极影响。该奖项反映了NSF的法定使命,并通过使用基金会的知识价值和更广泛的影响审查标准进行评估,被认为值得支持。
英文摘要
The NSF Convergence Accelerator supports use-inspired, team-based, multidisciplinary efforts that address challenges of national importance and will produce deliverables of value to society in the near future.This project will create an organization of data called a knowledge network that will allow doctors, researchers, the pharmaceutical industry, and citizen scientists to much more effectively understand and explore biomedicine. It will connect vast amounts of data in a way that allows important new questions to be asked, helping to discover the root of a biological process, identify cures for diseases, recognize pharmaceuticals that could be relevant previously unexplored conditions, and much more. The platform will enable and support biomedical applications created by third parties. The adoption of those enabled tools has the potential to have significant societal impacts: reducing healthcare costs, health disparities and accelerating therapeutics, ultimately improving the quality of life for every American.Healthcare costs Americans almost one-fifth of the entire US GDP. Health disparities, major public health issues, drug discovery complexity, and overall costs continue to grow dramatically. The mechanisms underlying human health are so complex that the human brain cannot integrate the ever-growing body of available knowledge relevant to treating patients or discovering therapies. This hampers the generation of new knowledge, specifically in the biomedical sciences and its implications for human health. The goal of this project, a biomedical open knowledge network (OKN), is to integrate billions of biomedical concepts into a knowledge engine that will enable doctors, drug developers, researchers, and citizen scientists to produce biologically meaningful answers to biomedical questions – rapidly and cheaply. This OKN will incorporate billions of factual relationships among biomedical concepts, allowing specialists to generalists to explore biomedicine in its whole might.The team supported by this project is part of a group pioneering the paradigm of knowledge networks in biomedicine. The effort brings together partners with expertise in search tools (including Google), graph theory (from Lawrence Livermore National Labs), and collaboration with the National Center for Advancing Translational Sciences’ Biomedical Data Translator (part of the National Institutes of Health), as well as working with other academic nonprofit institutions (the Institute for Systems Biology, Indiana University, UC San Diego, and Stanford. The ambitious effort represents convergence research including expertise across all aspects of biomedicine and data science, integrating doctors, researchers, epistemologists, database specialists, computer scientists, and statisticians.During Phase I of the Convergence Accelerator Program, the team developed and made available a fully functional biomedical knowledge network, the Scalable Precision Medicine Knowledge Engine (SPOKE, spoke.rbvi.ucsf.edu). This success supports the likelihood that the team will produce deliverables in this Phase II project that will have a positive impact on society.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
期刊论文(4)
专著(0)
科研奖励(0)
会议论文
DOI: 10.1002/aaai.12037
发表时间: 2022
期刊: AI MAGAZINE
影响因子: 0.9
作者: [Baranzini, Sergio E., Borner, Katy, Morris, John, Nelson, Charlotte A., Soman, Karthik, Schleimer, Erica, Keiser, Michael, Musen, Mark, Pearce, Roger, Reza, Tahsin, Smith, Brett, Herr, Bruce W., II, Oskotsky, Boris, Rizk-Jackson, Angela, Rankin, Katherine P., Sanders, Stephan J., Bove, Riley, Rose, Peter W., Israni, Sharat, Huang, Sui]
通讯作者: Huang, Sui
DOI: 10.1111/cts.13301
发表时间: 2022-08
期刊: Clinical and translational science
影响因子: --
作者: []
通讯作者:
Proto-OKN Theme 1: Connecting Biomedical information on Earth and in Space via the SPOKE knowledge graph
  • 批准号:
    2333819
  • 项目类别:
    Cooperative Agreement
  • 资助金额:
    $149.92万
  • 财政年份:
    2023
  • 负责人:
    Sergio Baranzini
  • 依托单位:
Convergence Accelerator Phase I (RAISE): A Multi-Scale Open Knowledge Network for Precision Medicine
国内基金
海外基金
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Multi-decadeurbansubsidencemonitoringwithmulti-temporaryPStechnique
  • 批准号:
    --
  • 项目类别:
    --
  • 资助金额:
    80万元
  • 批准年份:
    2022
  • 负责人:
    Timo Balz
  • 依托单位:
High-precision force-reflected bilateral teleoperation of multi-DOF hydraulic robotic manipulators
  • 批准号:
    52111530069
  • 项目类别:
    国际(地区)合作与交流项目
  • 资助金额:
    10万元
  • 批准年份:
    2021
  • 负责人:
    徐兵
  • 依托单位:
大地电磁强噪音压制的Multi-RRMC技术及其在青藏高原东南缘-印支块体地壳流追踪中的应用