Prognosis based network-type feature extraction for complex biological data

该项目旨在开发将高通量分子数据与生物学知识相结合的统计工具，以发现复杂疾病。该项目使用机器学习方法、统计模型和蛋白质组平台来确定临床病理和分子测量之间的关系。它将产生旨在加速生物医学研究中生物学和临床重大发现的进程的工具和见解。该项目将帮助澳大利亚统计研究人员和统计用户(来自生物、生态、医学、金融、农业和社会科学等不同领域)做出更好的、更容易理解的预测。

This project aims to develop statistical tools that integrate high-throughput molecular data with biological knowledge to make discoveries in complex diseases. This project uses machine learning methods, statistical models and proteomic platforms to identify relationships among clinico-pathologic and molecular measurements. It will produce tools and insights that are intended to accelerate the process of biologically and clinically significant discoveries in biomedical research. This project will help Australian researchers in statistics and users of statistics (from fields as diverse as biology, ecology, medicine, finance, agriculture and the social sciences) to make better predictions that are easier to understand.