Core E: Data Sciences Core

核心 E：数据科学核心

• 批准号: 10686050
• 负责人: Hakmook Kang
• 金额: $ 24.93万
• 依托单位: VANDERBILT UNIVERSITY MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-06 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10686050
• 关键词: Academic Medical Centers; Acceleration; Address; Affect; Animal Model; Applications Grants; Award; Behavioral; Big Data; Bioinformatics; Biometry; Caregivers; Collaborations; Complex; Consultations; Data; Data Analyses; Data Science; Data Science Core; Data Set; Databases; Disease; Down Syndrome; Education; Electronic Health Record; Faculty; Funding; Future; Generations; Genetic; Goals; Grant; Health; Housing; Human Resources; Image; Individual; Institution; Intellectual and Developmental Disabilities Research Centers; Intellectual functioning disability; Interdisciplinary Study; Link; Methodology; Methods; Modeling; Modernization; Outcome; Persons; Population; Productivity; Psychology; Rare Diseases; Records; Reproducibility; Research; Research Design; Research Personnel; Research Project Grants; Resources; Role; Sample Size; Sampling; Services; Source; Statistical Data Interpretation; Statistical Methods; Statistical Models; Structure; Talents; Techniques; Testing; Training; Training Activity; Universities; Work; autism spectrum disorder; biobehavior; biomedical imaging; clinical translation; complex data; data de-identification; data mining; data resource; disability; human model; improved; improved outcome; individualized medicine; informatics infrastructure; innovation; large datasets; large scale data; multimodal data; neuroimaging; neuroinformatics; neurophysiology; novel; patient oriented; programs; public health relevance; research and development; spatiotemporal; statistics; success; targeted treatment; tool; translational neuroscience

The success and impact of nearly every project in IDD hinges on the proper use of statistical techniques. Thus, Core E has a critical role in facilitating research for all IDDRC investigators, as well as for the progress of the other IDDRC Cores and Signature Research Project. Core E performs a unique function for IDDRC investigators as it helps them identify and use the statistical and methodological expertise and resources available at Vanderbilt University (VU) and Vanderbilt University Medical Center (VUMC) that are appropriate for their questions – especially for more complicated research designs (e.g., many layers of nesting) or those with statistical limitations (e.g., small sample sizes common in research with rare populations). Further, through generative activity with Clinical Translational and Translational Neuroscience Cores B and C, Core E provides sophisticated and non-trivial statistical methods and models tailored to IDD-related scientific questions (e.g., Bayesian spatio-temporal models for neuroimaging analysis). In addition to having considerable expertise in biostatistics, neuro-statistics, and quantitative psychology, Vanderbilt is also a national leader in developing big data structures and mining that data to advance health and development research, including the Synthetic Derivative (SD), a de-identified dataset of electronic health record data collected from over ~2.8 million total records. Though such big data structures are incredible resources to Vanderbilt, and especially IDDRC investigators with their ability to capture large samples of rare disorders, it can be challenging to put the data in analyzable formats and select suitable statistical approaches for analysis. Core E enables IDDRC investigators to fully capitalize on all these VU/VUMC resources through three aims: Aim 1, which provides access to modern statistical and data science methods to answer questions of relevance to IDD, including conducting data analyses for the Signature IDDRC Research Project; Aim 2, which enhances training in IDD research for those engaging in data science methods, including implementing a novel internal training grant program between Data Sciences Institute trainees and the IDDRC; and Aim 3, which supports innovation in health- related IDD research by facilitating use of large data sets such as the SD, including providing cutting-edge consultations and tools for working with large-scale SD IDD-curated database that IDDRC investigators can use for generating pilot data and conducting studies. Collectively, Core E’s aims and generative work and interactions with other IDDRC Cores not only meets the immediate needs of IDDRC investigators, but also anticipates future ones, by allowing for novel resources, platforms, and methods to be developed. By tackling and solving complex, multi-modal data science questions, Core E is poised to contribute substantially over the next 5 years to accelerating scientific discovery to improve the outcomes of people with IDDs.

几乎每个IDD项目的成功和影响都取决于统计技术的正确使用。因此，在本发明中， 核心E在促进所有IDDRC调查人员的研究以及 其他IDDRC核心和签名研究项目。核心E执行IDDRC的独特功能 调查人员，因为它有助于他们确定和使用统计和方法的专门知识和资源 可在范德比尔特大学（VU）和范德比尔特大学医学中心（VUMC）获得， 对于他们的问题-特别是对于更复杂的研究设计（例如，多层嵌套）或那些 由于统计限制（例如，小样本量在研究罕见人群时很常见）。此外，通过 临床转化和转化神经科学核心B和C的生成活动，核心E提供 为缺碘症相关科学问题量身定制的复杂和非平凡的统计方法和模型（例如， 神经影像分析的贝叶斯时空模型）。除了在以下方面具有相当的专业知识外， 生物统计学，神经统计学和定量心理学，范德比尔特也是一个国家领导人在发展大 数据结构和挖掘这些数据，以推进健康和发展研究，包括综合 Derivative（SD）是一个去识别的电子健康记录数据集，收集了超过280万的数据 记录尽管这样的大数据结构对范德比尔特来说是不可思议的资源，尤其是IDDRC 研究人员有能力捕获大量罕见疾病样本，因此将数据纳入其中可能具有挑战性。 可分析的格式，并选择合适的统计方法进行分析。核心E使IDDRC调查人员 通过三个目标充分利用所有这些VU/VUMC资源：目标1，提供访问 采用现代统计和数据科学方法回答与缺碘症有关的问题，包括 目标2，加强碘缺乏病研究方面的培训， 那些从事数据科学方法的人，包括实施一项新的内部培训资助计划 数据科学研究所学员与IDDRC之间的合作;以及支持健康创新的目标3- 通过促进使用可持续发展等大型数据集， 咨询和工具，用于使用IDDRC调查人员可以 用于生成试点数据和进行研究。总的来说，核心E的目标和生成性工作， 与其他IDDRC核心的交互不仅满足了IDDRC调查人员的直接需求， 通过允许开发新的资源、平台和方法来预测未来的发展。通过解决 以及解决复杂的多模态数据科学问题，Core E准备做出重大贡献。 在未来5年内，我们将加速科学发现，以改善缺碘症患者的预后。