Prognosis based network-type feature extraction for complex biological data

复杂生物数据的基于预测的网络型特征提取

• 批准号: DP170100654
• 负责人: Prof Jean Yang
• 金额: $ 24.81万
• 依托单位: The University of Sydney
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2017
• 资助国家: 澳大利亚
• 起止时间: 2017-05-01 至 2020-05-01
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP170100654
• 关键词: Prognosis; based; network; type; feature

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.

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学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（来自生物学、生态学、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。