Towards Integrative Data Analysis for Predictive Modeling in Biomedical Computing

生物医学计算中预测建模的综合数据分析

• 批准号: RGPIN-2015-06051
• 负责人: Mousavi, Parvin
• 金额: $ 3.13万
• 依托单位: Queen's University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=634268
• 关键词: Towards; Integrative; Data; Analysis; Predictive

This research is aimed at developing innovative methods to analyze medical data and understand the mechanisms of actions of complex disease, specifically prostate cancer (PCa). The new era of large-scale biology is characterized by insurmountable amounts of data including medical images, high throughput genetic information, large public databases, and clinical signals. Data abundance stands in contrast to our limited knowledge about the biological events underlying disease. The proposed research seeks to close this gap through a novel framework that uses state-of-the-art image analysis methods, and integrates molecular data and biological information, to formulate more accurate models to characterize cancer.

这项研究旨在开发创新的方法来分析医学数据并了解复杂疾病，特别是前列腺癌(PCa)的作用机制。新的大规模生物学时代的特点是数据量巨大，包括医学图像、高通量的遗传信息、大型公共数据库和临床信号。数据的丰富性与我们对疾病背后的生物事件的有限知识形成了鲜明对比。这项拟议的研究试图通过一个新的框架来弥合这一差距，该框架使用最先进的图像分析方法，并整合分子数据和生物信息，以制定更准确的模型来表征癌症。