Integrative Data Analytics Core - Core D

综合数据分析核心 - 核心 D

• 批准号: 10676789
• 负责人: Stephen CJ Parker
• 金额: $ 15.1万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-01 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10676789
• 关键词: Acceleration; Adipose tissue; Algorithm Design; Amplifiers; Artificial Intelligence; Atlases; Big Data; Cartilage; Clinical; Clinical Data; Communities; Complex; Computational Technique; Computer Assisted; Computer software; Computing Methodologies; Data; Data Analyses; Data Analytics; Data Collection; Data Storage and Retrieval; Decision Support Systems; Defect; Degenerative polyarthritis; Detection; Development; Diagnosis; Disease; Environment; Experimental Designs; Fracture; Functional Imaging; Future; Generations; Genome; Health; Human; Image; Image Analysis; Image Enhancement; Imaging Device; Imaging technology; Individual; Investments; Knowledge; Laboratories; Leadership; Machine Learning; Methodology; Methods; Michigan; Mission; Modality; Modeling; Molecular; Muscle; Musculoskeletal; Musculoskeletal Diseases; National Institute of Arthritis, and Musculoskeletal, and Skin Diseases; Outcome Study; Output; Patient-Focused Outcomes; Performance; Protocols documentation; Quality Control; Raman Spectrum Analysis; Randomized; Recommendation; Research; Research Design; Research Personnel; Resources; Science; Services; Source; Standardization; System; Techniques; Technology; Texture; Tissues; Training; Universities; Validation; Work; X-Ray Computed Tomography; analytical method; analytical tool; bone age; data management; deep learning; denoising; design; diverse data; epigenome; feature extraction; image processing; imaging Segmentation; improved; innovation; instrument; machine learning method; machine learning model; model organism; multidisciplinary; nano; novel; novel strategies; phenotypic data; pre-clinical; sensor; soft tissue; statistical and machine learning; structural imaging; tool; trait; transcriptome; webinar

Michigan Integrative Musculoskeletal Health Core Center – Core D Project Summary Diagnosis and management of musculoskeletal disorders is complex and best done with multidisciplinary teams and by analyzing data from a variety of complex sources. Advances in high-throughput ‘-omics’ technologies combined with large volumes of imaging and clinical data have created an unprecedented opportunity to better understand musculoskeletal disorders, improve patient outcomes, and reveal hidden information from clinical and pre-clinical data by applying integrative computational techniques. However, designing integrative models and computational approaches is challenging and requires expertise in omics, imaging and data analysis. The Integrative Data Analytics Core (Core D) of the Michigan Integrative Musculoskeletal Health Core Center (MiMHC) will provide expertise on data analysis in an integrative platform and promote new opportunities to MiMHC investigators by providing access to expertise in computational approaches. This core will be led by a multidisciplinary team of experts in omics, imaging and machine learning methodologies. Core D will provide expertise and scientific guidance in data analysis and help investigators plan well-powered randomized block study designs, interpret results, and provide recommendations for future studies. Core leadership will advise Center investigators on the best tools and methods for analyzing data and provide expertise in developing robust and validated systems using statistical and machine learning techniques. Core services will also include performing the data analytics task requested if Center investigators do not have the expertise. Thus, core services and advice will span the entire project, from idea inception, to data collection and management guidance, and to validated, integrative data analytics models and interpretation. This approach forms a unified model for planning, executing, and interpreting integrative studies across diverse laboratories, thereby enabling investigators to understand the data analytic models and the connection between their data and model outputs instead of just employing a “black box” approach. Thus, this core will accelerate science and generate new hypotheses by enabling individual investigators and broader teams to discover important parameters in the data and how these parameters are associated with study outcomes. Innovation plans include performing integrative analyses across omics, imaging, and clinical modalities using input data from different laboratories and species, thereby creating a novel, unified atlas of musculoskeletal signatures that spans Center investigators. This core will be impactful by developing new data analytics tools and designing new computer aided decision support systems aimed at understanding mechanisms of musculoskeletal disorders.

密歇根综合肌肉骨骼健康核心中心-核心D 项目摘要 肌肉骨骼疾病的诊断和管理是复杂的，最好由多学科团队完成 以及分析来自各种复杂来源的数据。高通量“组学”技术的研究进展 结合大量的成像和临床数据，创造了前所未有的机会， 了解肌肉骨骼疾病，改善患者预后，并揭示临床隐藏的信息， 和临床前数据。然而，设计综合模型 和计算方法是具有挑战性的，需要在组学，成像和数据分析的专业知识。的 密歇根综合肌肉骨骼健康核心中心（Michigan Integrative Musculoskeletal Health Core Center） （MiMHC）将在一个综合平台上提供数据分析方面的专业知识，并促进新的机会， MiMHC研究人员通过提供计算方法的专业知识。这个核心将由一个 在组学、成像和机器学习方法方面的多学科专家团队。核心D将提供 数据分析的专业知识和科学指导，并帮助研究者计划具有良好把握度的随机区组 研究设计，解释结果，并为未来的研究提供建议。核心领导将建议 为研究人员提供分析数据的最佳工具和方法，并提供开发强大的 并使用统计和机器学习技术验证系统。核心服务还包括 如果中心研究人员不具备专业知识，则执行所要求的数据分析任务。因此，核心 服务和建议将贯穿整个项目，从构思到数据收集和管理 指导，以及经过验证的综合数据分析模型和解释。这种方法形成了统一的 用于规划、执行和解释跨不同实验室的综合研究的模型， 调查人员了解数据分析模型及其数据和模型输出之间的联系 而不是仅仅采用“黑箱”方法。因此，这个核心将加速科学发展， 通过使个人研究者和更广泛的团队能够发现数据中的重要参数， 以及这些参数与研究结果之间的关系。创新计划包括执行综合 使用来自不同实验室和物种的输入数据进行跨组学、成像和临床模式的分析， 从而创建了一个新颖的、统一的肌骨骼特征图谱，该图谱涵盖了中心的研究人员。这一核心 通过开发新的数据分析工具和设计新的计算机辅助决策支持， 系统旨在了解肌肉骨骼疾病的机制。