ITR: Collaborative Research: Computational Learning and Discovery in Biological Sequence, Structure and Function Mapping

ITR：协作研究：生物序列、结构和功能绘图中的计算学习和发现

• 批准号: 0225636
• 负责人: Judith Klein
• 金额: --
• 依托单位: University of Pittsburgh
• 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=0225636&HistoricalAwards=false
• 关键词: ITR; Collaborative; Research; Computational; Learning

EIA-0225656Reddy, RajCarnegie Mellon UniversityTitle: Computational Learning and Discovery in Biological Sequence, Structure and Function MappingComputer scientists, together with biological chemists will collaborate using statistical and computational tools and methods that the computer scientists have been developing for dealing with human language to better understand the function of proteins. Proteins are major players in the functioning of human and all other living cells. As in languages, where sequences of letters determine patterns of words and sentences, sequences of amino acids in proteins determine protein structure, dynamics and function. Such sequences and their constituents can be thought of as syllables or words that have particular properties. Given these sequences, scientists want to be able to predict their geometrical structure and dynamics, and hence their function. A deeper understanding of the relationship between these is required so that the information hidden in the DNA sequences of genes can be used to develop drugs to fight disease. In particular, there is great societal demand to understand and treat degenerative diseases, many of which are based on defective triggers for protein shape and interactions. Work toward these goals requires deep knowledge both in computer science and in biological chemistry, and must therefore be collaborative in nature. Carnegie Mellon computer scientists will therefore be partnering with colleagues with expertise in Biological Chemistry at the University of Pittsburgh, the Massachusetts Institute of Technology (MIT), Boston University and the National Research Council of Canada. Industry collaborators include Mathworks, Inc., and medical bioinformatics company, Medstory, Inc. Using tools like statistical language modeling, machine learning methods and high-level language processing for understanding how proteins work inside cells is a relatively new field called computational biolinguistics. At this point, the researchers have been able to detect protein fragment signatures from pathogens by application of statistical language modeling technologies to genome sequences, promising novel strategies in identifying and targeting such pathogens. .

EIA-0225656 Reddy，RajCarnegie Mellon大学标题：生物序列、结构和功能映射中的计算学习和发现计算机科学家将与生物化学家一起合作，使用计算机科学家一直在开发的用于处理人类语言的统计和计算工具和方法，以更好地理解蛋白质的功能。蛋白质是人类和所有其他活细胞功能的主要参与者。就像在语言中一样，字母序列决定单词和句子的模式，蛋白质中的氨基酸序列决定蛋白质的结构、动力学和功能。 这样的序列和它们的成分可以被认为是具有特定属性的音节或单词。有了这些序列，科学家们希望能够预测它们的几何结构和动力学，从而预测它们的功能。需要更深入地了解这些之间的关系，以便隐藏在基因DNA序列中的信息可以用于开发对抗疾病的药物。特别是，有很大的社会需求，以了解和治疗退行性疾病，其中许多是基于蛋白质形状和相互作用的缺陷触发。实现这些目标的工作需要在计算机科学和生物化学方面的深厚知识，因此必须在本质上是协作的。因此，卡内基梅隆大学的计算机科学家将与匹兹堡大学、马萨诸塞州理工学院（MIT）、波士顿大学和加拿大国家研究理事会的生物化学专业同事合作。行业合作者包括Mathworks，Inc.，和医学生物信息学公司Medstory，Inc.使用统计语言建模，机器学习方法和高级语言处理等工具来了解蛋白质在细胞内如何工作是一个相对较新的领域，称为计算生物语言学。 在这一点上，研究人员已经能够通过将统计语言建模技术应用于基因组序列来检测病原体的蛋白质片段特征，这有望成为识别和靶向此类病原体的新策略。.