Data Center for Acute to Chronic Pain Biosignatures

急性至慢性疼痛生物特征数据中心

• 批准号: 10468273
• 负责人: Martin Lindquist
• 金额: $ 253.48万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10468273
• 关键词: Acute; Awareness; Behavior; Behavior assessment; Behavioral; Biometry; Cellular Phone; Clinical; Clinical Data; Cloud Computing; Code; Collaborations; Communication; Communities; Computer software; Country; Custom; Data; Data Analyses; Data Science; Data Scientist; Data Set; Data Storage and Retrieval; Databases; Development; Education; Educational workshop; Electronic Health Record; Enrollment; Environment; Epidemic; Faculty; Funding; Genomics; High Performance Computing; Image; Informatics; Infrastructure; Institution; Internet; Intranet; Lead; Machine Learning; Measures; Metadata; Methods; Modeling; Pain; Patients; Peripheral; Persons; Physiology; Process; Productivity; Proteomics; Protocols documentation; Pythons; Quality Control; Recording of previous events; Research Personnel; Resources; Rest; Running; Science; Scientist; Sensory; Side; Software Engineering; Standardization; Structure; System; Testing; Texas; Training; Training Programs; United States National Institutes of Health; Universities; Update; Visualization; Wages; Wearable Computer; Work; analysis pipeline; base; biosignature; chronic pain; clinical pain; cloud based; cluster computing; cyber infrastructure; data access; data centers; data fusion; data integration; data pipeline; data portal; data quality; data resource; data submission; data tools; deep learning; design; diverse data; electronic book; experience; health data; massive open online courses; meetings; member; metaportal; multimodal data; neuroimaging; online course; opioid use; outreach; outreach program; pain chronification; predictive modeling; programs; prospective; response; skills; social media; statistics; tool; user-friendly

Understanding the mechanisms underlying the transition to chronic pain is a key to mitigating the dual epidemics of chronic pain and opioid use in the U.S. In response to RFA-RM-18-031, and as part of the NIH Common Fund Acute to Chronic Pain Signatures (A2CPS) Program, we will establish a Data Integration and Resource Center (DIRC) to integrate imaging, physiology, -omics, behavioral, and clinical data to develop biosignatures for the transition to chronic pain. The Center will be based in the Department of Biostatistics at JHU, a nexus for a wide range of collaborators with expertise in (1) advanced data science and machine learning, (2) neuroimaging, (3) genomics and related -omics, (4) wearable computing and smartphone-based behavioral assessment, (5) systems-level predictive biosignatures, (6) software engineering and high-performance computing, and (7) world-renowned pain researchers from JHU and other institutions. JHU Biostatistics is the top-ranked department of its kind in the country, and its unique blend of faculty provides the ability to be nimble and accommodate analysis of diverse data types as needed, and a unique capacity for scientific outreach through online courses and other forums. To deliver computing infrastructure and cloud-based computing for A2CPS, we partner with the Texas Advanced Computing Center (TACC), who have a long track record of large-scale collaborations and have already built many of the cloud computing tools we see as ideal for this project. The Center will consist of three components and an Administrative Core. The Administrative Core will lead the Center and facilitate interaction among the components of the Center and across the A2CPS consortium. The Data Coordination Component (DCC) will provide the infrastructure for storage and processing, analysis pipelines, cloud computing, and portals for data upload/query/export, in addition to other technical deliverables. The Data Integration and Analysis Component (DIAC) will provide data type-specific content for pipelines and analyses of data collected by the A2CPS consortium. The Scientific Outreach Component (SOC) will use DCC- developed portal infrastructure to maintain the consortium intranet and perform outreach via the public A2CPS portal. It will also organize a variety of in-person and online training and outreach programs, including the creation of free, online courses disseminating information about chronic pain and A2CPS tools, data, and models.

了解转化为慢性疼痛的潜在机制是治疗 缓解美国慢性疼痛和阿片类药物使用的双重流行病 RFA-RM-18-031，并作为NIH急性到慢性疼痛共同基金的一部分 签名(A2CPS)计划，我们将建立数据集成和资源中心 (DIRC)集成成像、生理学、组学、行为和临床数据以开发 向慢性疼痛过渡的生物特征。该中心将设在 JHU生物统计系，广泛的合作者与 具备(1)高级数据科学和机器学习、(2)神经成像、(3) 基因组学和相关组学，(4)可穿戴计算和基于智能手机的行为 评估，(5)系统级预测性生物签名，(6)软件工程和 高性能计算，以及(7)来自JHU和 其他机构。JHU生物统计学是同类学科中排名最高的 国家，其独特的教师混合提供了灵活和 根据需要适应对不同数据类型的分析，并具有独特的 通过在线课程和其他论坛进行科学宣传。要提供计算 针对A2CPS的基础设施和云计算，我们与德克萨斯州 高级计算中心(TACC)，他们拥有长期的大规模 并且已经构建了许多我们认为理想的云计算工具 为了这个项目。该中心将由三个组成部分和一个行政核心组成。 行政核心将领导该中心，并促进 中心和整个A2CPS财团的组件。数据协调 组件(DCC)将为存储和处理、分析提供基础设施 用于数据上传/查询/导出的管道、云计算和门户，以及其他 技术交付成果。数据集成和分析组件(DIAC)将 为管道提供特定于数据类型的内容，并分析 A2CPS财团。科学外联部分(SOC)将使用DCC-- 开发门户基础设施，以维护财团内联网并进行外联 通过公共A2CPS门户网站。它还将组织各种面对面和在线培训 和外展方案，包括创建免费的在线课程，传播 有关慢性疼痛和A2CPS工具、数据和模型的信息。