Data-Analysis-Core

数据分析核心

• 批准号: 10376491
• 负责人: Qin Ma
• 金额: $ 53.75万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-30 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10376491
• 关键词: Artificial Intelligence; Atlases; Bioinformatics; Biological; Biological Assay; Biological Markers; Biometry; Cell Aging; Cells; Clinical Sciences; Collaborations; Computational Biology; Data; Data Analyses; Data Collection; Data Coordinating Center; Data Set; Database Management Systems; Databases; Doctor of Philosophy; Elements; Ensure; Goals; Health Sciences; Heart; Human; Image; Informatics; Institution; Lung; Machine Learning; Maps; Medical center; Methods; Modeling; Multiomic Data; Ohio; Procedures; Process; Public Health; Quality Control; Research; Resource Sharing; System; Tissues; Translational Research; Universities; Validation; Work; base; biomedical informatics; computational intelligence; computational pipelines; computer science; data harmonization; data integrity; data quality; data standards; design; experience; imaging modality; intelligent algorithm; metadata standards; mid-career faculty; open data; professor; programs; quality assurance; senescence; synergism; three-dimensional modeling

ABSTRACT Data Analysis Core. The Data Analysis Core (DA) will construct biomarker and map datasets generated from whole human lung and heart tissue, cells, and tissue-based 3D models. The DAC will deliver robust and standardized data to the TriState SenNet TMC and the SenNet Data Coordinating Center (CODCC). The overall goals of the DAC are to store, analyze, and model the biomarker and map datasets generated from the tissues provided by the Biospecimen Core (BC) and analyzed by the Biological Analysis Core (BAC) using robust computational packages and analysis methods, align with data from the BC, and work together with other TMCs and the CODCC to develop network-wide open data and metadata standards, and to conduct cross-validation of assays within and across TMCs. We will achieve these goals by: 1) providing biostatistics and informatics support to ensure the integrity of data collection, storage, transfer, and harmonization within the TriState SenNet TMC; 2) constructing a lung and heart senescence atlas using advanced and robust statistical, computational, and artificial intelligence algorithms; and 3) collaborating closely with the CODCC and other TMCs on developing open data and metadata standards and cross-validating assays within and across TMCs.

摘要 数据分析核心数据分析核心（DA）将构建生物标志物和地图数据集， 完整的人体肺和心脏组织、细胞和基于组织的3D模型。发援会将提供稳健和 标准化数据提供给TriState SenNet TMC和SenNet数据协调中心（CODCC）。整体 DAC的目标是存储、分析和建模从组织生成的生物标志物和地图数据集 由生物样本中心（BC）提供，并由生物分析中心（BAC）使用耐用的 计算软件包和分析方法，与来自BC的数据保持一致，并与其他TMC协同工作 和CODCC开发全网络开放数据和元数据标准，并进行交叉验证 在TMCs内和跨TMCs进行检测。我们将通过以下方式实现这些目标：1）提供生物统计学和信息学 支持确保三州SenNet内数据收集、存储、传输和协调的完整性 TMC; 2）使用先进和稳健的统计，计算， 和人工智能算法;以及3）与CODCC和其他TMC密切合作， 开放数据和元数据标准以及TMC内部和跨TMC的交叉验证分析。