Postdoctoral Research Fellowship in Interdisciplinary Informatics for FY 2003

2003财年跨学科信息学博士后研究奖学金

• 批准号: 0306095
• 负责人: Debra Goldberg
• 金额: $ 10万
• 依托单位: Goldberg Debra S
• 依托单位国家: 美国
• 项目类别: Fellowship Award
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-07-01 至 2005-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0306095&HistoricalAwards=false
• 关键词: Postdoctoral; Research; Fellowship; Interdisciplinary; Informatics

Postdoctoral Research Fellowships in Interdisciplinary Informatics are sponsored jointly by the Directorates for Mathematical and Physical Sciences (MPS) and Biological Sciences (BIO) to encourage research and training that cross the traditional disciplinary boundaries between them. These fellowships provide opportunities for interdisciplinary research and educational activities in biology and informatics to a wide range of recent doctoral recipients (biologists, chemists, physicists, mathematicians, statisticians, computer scientists, and others). It is expected that the Fellows trained through these fellowships will play an important role in training the future workforce. Postdoctoral research and training in informatics will permit junior scientists trained in biology, mathematical, chemical, and physical sciences to play key roles in developing new quantitative tools and methods that will advance informatics in biology and other fields.The research and training plan is entitled "Protein function prediction from error-prone genomic data using confidence weights and network topology." High-throughput experimental methods are both expensive and error-prone. Still, the underlying biology generates patterns in the global network topology. This study integrates diverse functional genomic data using probabilistic edge weights based on experimental data and shows how each observation fits the expected local graph topology to infer protein function.

跨学科信息学博士后研究奖学金由数学和物理科学（MPS）和生物科学（BIO）理事会联合赞助，以鼓励跨越它们之间传统学科界限的研究和培训。这些奖学金为生物学和信息学的跨学科研究和教育活动提供了机会，以广泛的近期博士获得者（生物学家，化学家，物理学家，数学家，统计学家，计算机科学家等）。预期通过这些研究金培训的研究员将在培训未来劳动力方面发挥重要作用。信息学方面的博士后研究和培训将允许生物学、数学、化学和物理科学方面的初级科学家在开发新的定量工具和方法方面发挥关键作用，这些工具和方法将推动生物学和其他领域的信息学。这项研究和培训计划的标题是“利用置信度权重和网络拓扑从易出错的基因组数据中预测蛋白质功能”。高通量实验方法既昂贵又容易出错。然而，潜在的生物学在全球网络拓扑结构中产生了模式。本研究使用基于实验数据的概率边权整合了多种功能基因组数据，并展示了每个观察结果如何符合预期的局部图拓扑来推断蛋白质功能。