ITR: Language, Learning, and Modeling Biological Sequences

ITR：语言、学习和生物序列建模

• 批准号: 0205456
• 负责人: Aravind Joshi
• 金额: $ 349.98万
• 依托单位: University of Pennsylvania
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-09-15 至 2008-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0205456&HistoricalAwards=false
• 关键词: ITR; Language; Learning; Modeling; Biological

EIA-0205456Joshi. Aravind KUniversity of PennsylvaniaITR: Language, Learning, and Modeling Biological SequencesRecent significant advances in natural language processing such as the integration of grammatical and probabilistic machine-learning techniques have not been exploited for modeling biological sequences. These new techniques are highly relevant to the biological domain because they support the integration of sequence features at several scales, from dependencies between successive items through dependencies involving complex structures to overall sequence statistics. Hence, the major goals to be pursued are: (1) Development of new techniques for integrating grammatical and probabilistic information, in particular, integration and evaluation of grammatical, probabilistic, and approximate counting methods for fold prediction in secondary and tertiary structures of biomolecules. (2) Development and evaluation of probabilistic exponential models for gene finding, in particular genes for apicoplast-targeted proteins in eukaryotic human pathogens of the phylum `Apicomplexa'. This research is highly interdisciplinary, involving the disciplines of computer science, biology and linguistics. It will have a significant impact on the modeling of biological sequences. It will also provide a wonderful opportunity to train new researchers to carry out this interdisciplinary research, thus contributing to science and mathematical education and human resource development. The proposed research arose out of many discussions that took place at a landmark workshop on `Language Modeling of Biological Data' held at the University of Pennsylvania in February 2001.

EIA-0205456乔希。 Aravind K 宾夕法尼亚大学ITR：语言、学习和生物序列建模最近自然语言处理方面的重大进展（例如语法和概率机器学习技术的集成）尚未用于生物序列建模。这些新技术与生物领域高度相关，因为它们支持多个尺度上序列特征的整合，从连续项之间的依赖关系到涉及复杂结构的依赖关系，再到整体序列统计。因此，要追求的主要目标是：（1）开发整合语法和概率信息的新技术，特别是整合和评估用于生物分子二级和三级结构折叠预测的语法、概率和近似计数方法。 (2) 开发和评估用于基因发现的概率指数模型，特别是“顶端复合体”门真核人类病原体中顶端质体靶向蛋白质的基因。这项研究具有高度跨学科性，涉及计算机科学、生物学和语言学等学科。它将对生物序列的建模产生重大影响。它还将为培训新研究人员进行跨学科研究提供绝佳的机会，从而为科学和数学教育以及人力资源开发做出贡献。这项拟议的研究源于 2001 年 2 月在宾夕法尼亚大学举办的一次具有里程碑意义的“生物数据语言建模”研讨会上进行的多次讨论。