ITR-(ASE+NHS)-(dmc): Rational Genomic Annotation Systems: Integration, Mining and Modeling of Biological Data

ITR-(ASE NHS)-(dmc)：Rational 基因组注释系统：生物数据的集成、挖掘和建模

• 批准号: 0428715
• 负责人: Simon Kasif
• 金额: --
• 依托单位: Trustees of Boston University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-09-15 至 2009-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0428715&HistoricalAwards=false
• 关键词: ITR; ASE+NHS; dmc; Rational; Genomic

Boston University has been awarded a grant to develop Rational Genomic Annotation Systems for integration, mining and modeling of biological data. The research, which includes scientists and students at Massachusetts Institute of Technology and Tufts University, is aimed at developing a systematic methodology for the integration of multiple sources of evidence involving both predictive inference and learning. The PIs propose to develop an information-integration framework based on probabilistic graphical models, with a modular interface that will support different data types. This research will facilitate the development of a new generation of extensible gene annotation systems that will provide a greater coverage and accuracy than existing systems. It will also produce new computational algorithms to support these novel forms of integration as well as a systematic new methodology for building novel probabilistic model integrators. This research builds on a collaborative effort of several investigators emphasizing interdisciplinary research and education, training of a new generation of leaders with expertise in both system biology and computing sciences. The infrastructure and techniques proposed are aimed to provide the basic tools for novel wide international community efforts involving both computational biologists and experimental scientists to produce and validate the functional role of thousands of newly sequenced genes.

波士顿大学获得了一笔赠款，用于开发用于生物数据集成、挖掘和建模的理性基因组注释系统。这项研究包括马萨诸塞州理工学院和塔夫茨大学的科学家和学生，旨在开发一种系统的方法，用于整合涉及预测推理和学习的多种证据来源。PI建议开发一个基于概率图形模型的信息集成框架，该框架具有支持不同数据类型的模块化接口。这项研究将促进新一代可扩展基因注释系统的开发，该系统将提供比现有系统更大的覆盖范围和准确性。它还将产生新的计算算法，以支持这些新形式的整合，以及一个系统的新方法，建立新的概率模型积分。这项研究建立在几位研究人员的合作努力之上，强调跨学科研究和教育，培养具有系统生物学和计算科学专业知识的新一代领导人。提出的基础设施和技术的目的是提供新的广泛的国际社会的努力，包括计算生物学家和实验科学家的基本工具，以生产和验证数以千计的新测序基因的功能作用。