ITR: Language, Learning, and Modeling Biological Sequences

EIA-0205456乔希。Aravind KUniversity of Pennsylvania ITR：Language，Learning，and Modeling Biological SequencesNatural Language Processing的最新重大进展，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。这些新技术与生物学领域高度相关，因为它们支持在几个尺度上整合序列特征，从连续项目之间的依赖关系到涉及复杂结构的依赖关系到整体序列统计。因此，要追求的主要目标是：（1）开发新技术，用于整合语法和概率信息，特别是整合和评估语法，概率和近似计数方法，用于生物分子二级和三级结构的折叠预测。(2)开发和评价基因发现的概率指数模型，特别是在“顶复体”门的真核人类病原体中的顶质体靶向蛋白基因。这项研究是高度跨学科的，涉及计算机科学，生物学和语言学的学科。它将对生物序列的建模产生重大影响。它还将提供一个极好的机会，培养新的研究人员进行跨学科的研究，从而有助于科学和数学教育和人力资源开发。2001年2月在宾夕法尼亚大学举行的"生物数据的语言建模“的里程碑式研讨会上进行了多次讨论，提出了拟议的研究。

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.