Multidimensional in vivo Assessments of Engineered Nanomaterials and Biological Interactions

工程纳米材料和生物相互作用的多维体内评估

• 批准号: 10381394
• 负责人: Robyn L Tanguay
• 金额: $ 10.49万
• 依托单位: OREGON STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10381394
• 关键词: Biological; Biological Assay; Complex; Data; Data Sources; Engineering; Glean; Goals; Health; Information Dissemination; Laboratories; Machine Learning; Malignant Neoplasms; Modeling; Nanotechnology; Pattern; Research; data structure; design; diverse data; flexibility; in vivo; nanomaterials; Biological; Biological Assay; Complex; Data; Data Sources; Engineering; Glean; Goals; Health; Information Dissemination; Laboratories; Machine Learning; Malignant Neoplasms; Modeling; Nanotechnology; Pattern; Research; data structure; design; diverse data; flexibility; in vivo; nanomaterials

Abstract We propose an integrative modeling approach to synthesize data generated across labs of the Nanotechnology Health Implications Research (NHIR) consortium. Our goal is to leverage these diverse data toward developing principles for sustainable design and synthesis of engineered nanomaterials (ENM). Our approach advances this goal by (1) Creation of a flexible data structure that permits analysis across ENM attributes and assay results, (2) Sophisticated machine learning modeling of integrated data, and (3) Facilitating dissemination of results via Cancer Nanotechnology Laboratory (CaNanoLab). This proposal can advance overarching NHIR goals by allowing for the discovery of robust patterns in complex, integrated data that could not be gleaned from single data sources alone.

抽象的 我们提出了一种综合建模方法，以合成跨实验室生成的数据 纳米技术健康影响研究（NHIR）财团。我们的目标是利用这些 多种数据朝着开发可持续设计和合成工程的原理 纳米材料（ENM）。我们的方法通过（1）创建灵活的数据结构来推动这一目标 允许跨ENM属性和测定结果分析，（2）复杂的机器学习建模 综合数据，以及（3）通过癌症纳米技术实验室促进结果的传播 （Cananolab）。该提议可以通过允许发现 复杂的集成数据中的强大模式，仅单独的数据源无法收集。