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BD Spokes: PLANNING: SOUTH: Collaborative: Rare Disease Observatory

BD Spokes: PLANNING: SOUTH: Collaborative: Rare Disease Observatory
BD 发言人:规划:南方:协作:罕见疾病观察站
批准号:
1636733
负责人:
Rada Chirkova
金额:
$7.11万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2016
资助国家:
美国
项目状态:
已结题
起止时间:
2016-09-01 至 2018-08-31

项目摘要

项目成果

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中文摘要
翻译
在过去的15年里,由于对人类遗传学和相关医学综合征的理解迅速扩展,现已发现了7000多种罕见疾病。在美国,如果一种疾病在一个特定的罕见疾病组中影响不到20万人,则被认为是罕见的。据估计,十分之一的美国人-超过3000万人-患有罕见疾病。大约50%的罕见病患者是儿童;此外,80%的罕见病是遗传性的,因此存在于整个人身上。的生活。如果有关罕见疾病的现有信息是连接在一起的,并且可以作为一个整体访问,那么就有可能利用大型数据集分析和生物医学分析的最新进展来处理这些信息。因此,对罕见疾病的研究和临床进展有望改善患者及其家属的状况和生活质量。这些数据还将使研究人员、临床医生、医疗保健系统、行业和机构受益,这些机构迄今为止还没有成功地协调其活动以应对这些数据挑战。最广泛的影响将是证明这种模式的协调和整合是可能的,因此可能有助于解决影响数千万美国人的大型医疗保健问题。 拟议规划项目的具体目标是:(i)探讨将以前孤立的、因此只能在单一研究小组或机构内使用的罕见疾病高价值数据集纳入罕见疾病观察站的可行性;(ii)研究如何使这些数据集沿着适当的隐私和访问控制机制,作为一个整体提供给更广泛的群体和广大公众。在这项试点探索中,该团队将对可用的罕见疾病数据集进行编目,调查北卡罗来纳州内的相关结构或政策,并探索RDO的数据支持和信息集成解决方案。根据吸取的经验教训,该团队以后可以将互动扩展到其他南BD中心州,并可能扩展到全国各地的其他中心。如果拟议的规划项目证实了RDO的可行性,它将向NSF提交一份完整的BD Spoke提案,重点是创建一个大规模的罕见疾病数据集集成分析就绪存储库,据我们所知,这是全国第一个这样的存储库。这将使联合收割机能够将整合的罕见疾病数据与大数据分析和生物医学分析的最新进展相结合。因此,对罕见疾病的研究和临床进展预计将导致新的研究和科学发现与合作,并最终改善患者及其家属的状况和生活质量。
英文摘要
Due to the rapid expansion in understanding human genetics and related medical syndromes over the past 15 years, over 7000 rare diseases have now been identified. In the United States, a condition is considered rare if it affects fewer than 200,000 persons combined in a particular rare-disease group. It is estimated that one in ten Americans - more than 30 million people - are living with rare diseases. Approximately 50% of the people affected by rare diseases are children; further, 80% of rare diseases are genetic in origin, and thus are present throughout a person?s life. If the available information about rare diseases were connected and accessible as a whole, it would be possible to work with it using the latest advances in large data-set analytics and biomedical analysis. The research and clinical progress on rare diseases thus enabled will be expected to lead to improvements in the condition and quality of life of patients and their families. These data will also benefit researchers, clinicians, healthcare systems, industry, and agencies, which have so far not been successful at coordinating their activities to address these data challenges. The broadest impact would be to demonstrate that this model coordination and integration is possible and thus may be useful in addressing the large healthcare problems that affects tens of millions of Americans. The specific goals of the proposed planning project are to (i) explore the feasibility of integrating into a Rare Disease Observatory (RDO) high-value data sets on rare diseases that were previously siloed and, therefore, usable only within a single research group or institution, and to (ii) investigate how to make these data sets, along with appropriate privacy and access-control mechanisms, available as a whole to a broader set of groups and to the public at large. In this pilot exploration, the team will catalog the available rare-disease data sets, investigate the relevant structures or policies within the state of North Carolina, and explore data-enablement and information-integration solutions for RDO. Based on the lessons learned, the team could later extend interactions to other South BD Hub states, and possibly to other Hubs across the nation. If the proposed planning project confirms feasibility of RDO, it will enable submission to NSF of a full BD Spoke proposal focusing on creating a large-scale integrated analysis-ready repository of data sets on rare diseases, the first such repository in the nation to the best of our knowledge. This will allow to combine the integrated rare-disease data with the latest advances in large-data analytics and biomedical analysis. The research and clinical progress on rare diseases thus enabled would be expected to lead to new research and science discoveries and collaborations, and ultimately to improvements in the condition and quality of life of patients and their families.
期刊论文(1)
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会议论文
DOI: 10.1109/bigdata.2017.8258302
发表时间: 2017-12
期刊: 2017 IEEE International Conference on Big Data (Big Data)
影响因子: --
作者: [Ran Tan;Rada Y. Chirkova;V. Gadepally;T. Mattson]
通讯作者: Ran Tan;Rada Y. Chirkova;V. Gadepally;T. Mattson
Phase 1 IUCRC NC State University: Center for Accelerated Real Time Analytics (CARTA)
  • 批准号:
    1747555
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $74.76万
  • 财政年份:
    2018
  • 负责人:
    Rada Chirkova
  • 依托单位:
I/UCRC Planning Grant: Site Addition to CHMPR I/UCRC
  • 批准号:
    1439670
  • 项目类别:
    Standard Grant
  • 资助金额:
    $1.38万
  • 财政年份:
    2014
  • 负责人:
    Rada Chirkova
  • 依托单位:
CAREER: Adaptive Automated Design of Stored Derived Data
  • 批准号:
    0447742
  • 项目类别:
    Standard Grant
  • 资助金额:
    $0.0万
  • 财政年份:
    2005
  • 负责人:
    Rada Chirkova
  • 依托单位:
Efficient View-Design Algorithms to Achieve Near-Optimal Performance of Sets of Relational Queries
  • 批准号:
    0307072
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $25.32万
  • 财政年份:
    2003
  • 负责人:
    Rada Chirkova
  • 依托单位:
海外基金