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: 298230-2010
• 负责人: Mousavi, Parvin
• 金额: $ 2.26万
• 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: 2013
• 资助国家: 加拿大
• 起止时间: 2013-01-01 至 2014-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=525928
• 关键词: Towards; integrative; data; analysis; predictive

The proposed research is aimed at developing innovative methods to support our understanding of complex biological phenomena. The new era of large-scale biology is characterized by insurmountable amounts of data including high throughput genetic data, large public databases, medical images and clinical signals. Data abundance stands in contrast to the limited knowledge about the biological events underlying disease. The proposed research seeks to close this gap through the development of feature extraction methods that exploit multi-modal biomedical data for informative attributes, and modeling approaches that integrate these attributes to explain disease processes. The computational methods sought will advance the state of the art in machine learning, image and signal processing, and software engineering.

这项研究旨在开发创新方法，以支持我们对复杂生物现象的理解。大规模生物学的新时代的特征是不可克服的数据量，包括高通量遗传数据、大型公共数据库、医学图像和临床信号。数据丰富与对疾病背后的生物事件的有限了解形成了鲜明对比。拟议的研究旨在通过开发利用多模态生物医学数据的信息属性的特征提取方法，以及集成这些属性以解释疾病过程的建模方法来缩小这一差距。所寻求的计算方法将推进机器学习，图像和信号处理以及软件工程的最新技术水平。