Resource Development Core

抽象-资源开发核心(RDC) 密歇根大学奥布莱恩肾脏翻译资源中心(MKTC)的主要目标是 提供依赖系统生物学方法开发的有效翻译研究渠道 肾脏疾病患者的新的诊断、预后和治疗策略。我们的资源 开发核心(RDC)将支持应用系统生物学核心(ASBC)，即数据分析服务 核心(DASC)和整个肾脏研究社区，通过开发一个统一的平台和方法 整合多维数据、队列选择和临床试验设计。密歇根大学精密分校 健康倡议(PHI)，除了社会数据外，它还包括来自密歇根医学患者的所有数据 健康和生物样本的决定因素，是区域发展委员会将扩展到的独特资源 开发以下管道： A.生物标记物候选者的选择、分析、验证和纳入研究设计。刚果民盟将 建立数据库和渠道，为研究人员提供最有前景的生物标志物、相关分析 基于调查结果，以及围绕验证和质量控制的指导。 B.队列发展。我们将利用PHI的基础设施来构建算法来识别和管理 具有高度自信的肾脏特异性诊断、相关临床特征、实验室检测、社会 健康决定因素，以及通过集成多个数据源(包括帐单代码和 自然语言处理。最终结果将使调查人员能够轻松地识别相关的 并以可通过图形用户界面进行分析的格式快速检索数据。 C.肾脏结果。我们将建立一个渠道，纳入美国肾脏数据系统(USRDS) 将数据输入PHI平台，丰富了密歇根医学数据集，并为队列提供了验证性数据- 具体分析。 新的代谢组学技术和多组学整合。除了发展空间代谢学 平台，我们改进了非靶向代谢组学数据的注释工具和识别新的 化合物，并提供代谢组学-蛋白质组-转录组学-表观基因组学的新的综合分析 数据。 临床试验设计。我们将建立肾脏特异性临床试验设计和实施的工作流程， 以及通过非常成功的临床试验学院提供相关的研讨会。 区域发展委员会将建立的管道和工作流程将为各领域的调查人员提供 研究阶段-从BASE(生物标记物发现)到床边(临床试验设计)-整合 多维数据-从健康的社会决定因素到有机生物标记物和结果-在 应对肾脏疾病患者管理的主要挑战。

ABSTRACT - RESOURCE DEVELOPMENT CORE (RDC) The main goal of the University of Michigan O'Brien Kidney Translational Resource Center (MKTC) is to provide an effective translational research pipeline that relies on a systems biology approach to develop novel, diagnostic, prognostic and therapeutic strategies for patients with kidney diseases. Our Resource Development Core (RDC) will support the Applied Systems Biology Core (ASBC), the Data Analytic Services Core (DASC) and the kidney research community at large by developing a unified platform and methods that integrate multidimensional data, cohort selection and clinical trial design. The University of Michigan Precision Health Initiative (PHI), which incorporates all data from Michigan Medicine patients in addition to social determinants of health and biological samples, represents a unique resource that the RDC will expand upon to develop the following pipelines: a. Biomarker candidate selection, assay, validation, and incorporation into study design. The RDC will establish a database and pipeline to provide investigators the most promising biomarkers, relevant assays based on the outcomes investigated, in addition to guidance surrounding validation and quality control. b. Cohort development. We will leverage the infrastructure of the PHI to build algorithms to identify and curate with high confidence kidney-specific diagnoses, relevant clinical characteristics, laboratory testing, social determinants of health, well as outcomes through integrating multiple data sources including billing codes and natural language processing. The end-result will provide investigators the ability to easily identify relevant cohorts and rapidly retrieve data in a format that is ready to analyze through a graphical user interface. c. Kidney outcomes. We will establish a pipeline to incorporate United States Renal Data System (USRDS) data into the PHI platform, enriching the Michigan Medicine dataset and providing confirmatory data for cohort- specific analyses. d. Novel metabolomic technologies and multi-omic integration. In addition to developing spatial metabolomic platform, we improve tools for annotation of untargeted metabolomics data and identification of new compounds and provide novel integrative analysis of metabolomics-proteomics-transcriptomics- epigenomics data. e. Clinical trial design. We will establish a workflow for kidney-specific clinical trial design and implementation, as well as provide associated workshops through the highly successful Clinical Trials Academy. The pipelines and workflows to be established by the RDC will provide investigators across the spectrum of research stages – from bench (biomarker discovery) to bedside (clinical trial design) – the ability to integrate of multidimensional data – from social determinants of health to organic biomarkers and outcomes – in addressing the major challenges the management of patients with kidney disease.