Towards integrative data analysis for predictive modeling in biomedical computing

这项拟议的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。新的大规模生物学时代的特点是数据量巨大，包括高通量的基因数据、大型公共数据库、医学图像和临床信号。数据的丰富性与关于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开发利用多模式生物医学数据获取信息性属性的特征提取方法，以及集成这些属性来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动机器学习、图像和信号处理以及软件工程的最先进水平。

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.