III/SGER: Hypothesis Based Query and Verification of Pathway Models

III/SGER：基于假设的路径模型查询和验证

• 批准号: 0849207
• 负责人: Mark Musen
• 金额: --
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-01 至 2010-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0849207&HistoricalAwards=false
• 关键词: III; SGER; Hypothesis; Based; Query

Advancing knowledge in the biological sciences involvesexperimentally testing hypothesesand interpreting the results based on prior scientific work. Researchers face the challenge of collecting, evaluating and integrating large amounts of different kinds of information about organisms, cells, genes and proteins to generate a validhypothesis. And once a hypothesis is generated, the challenge is to evaluate the hypothesis with respect to what is already known. Our proposed research will:1. Test the scalability and extensibility of a novel computer system that allows biologists to construct and evaluate alternative hypotheses against a knowledge base on the yeast Saccharomyces cerevisiae. 2. Test a knowledge archive that supports the archiving and search of validated hypotheses. In our work, we define a hypothesis as a statement about relationships between components of a biological system that are intended to explain experimental observations. A set of validated hypotheses can be used as building blocks to construct larger, more complex models such as pathways. We plan to develop and test a new paradigm: that of hypothesis-driven querying of model organism knowledgebases. The proposed work, called HyQue (for Hypothesis-based Querying of pathway models), will take as input working hypotheses aboutpathway models expressed in a knowledge-based formalism, evaluate their consistency using existing data in a knowledgebase, and provide as output contradictory evidence and suggestions forimproving hypotheses. HyQue will incorporate formal knowledge representations based upon Semantic Web standards and an ontology to represent biological objects and relationships.HyQue will also contain a library of rules that determine counts of support and contradiction for a given hypothesis. We will prototype an archive of hypotheses that allows users to compare their hypothesis with other hypotheses submitted by their peers. We will explore the capability to: (1) express working hypotheses about the yeast cell cycle; (2) provide integration of data in the Saccharomyces Genome Database to evaluate/test pathway-specific hypotheses; and (3) archive these results. As analytical tools and database resources proliferate, biologists require facilities to integrate existing data into knowledge that can create a shared understanding ofbiological models. Our work will explore the expressivity and scalability of unique and novel querying and contradiction based reasoning methods that use rich formal knowledge specifications ofbiological events in order to accomplish information integration. Our proposed work will lead to a novel paradigm of querying biological knowledge that can dynamically retrieve, integrate andinterpret information in terms of biologically relevant relationships asserted as pathway models. Our work will examine the value of Semantic Web technologies in building a knowledgebase for such querying and reasoning and will aid in standardizing models of biological knowledge and add momentum to a range of ongoing ontology building efforts. Further information on the project can be found at the project web page: http://nigam.web.stanford.edu/hyque

推进生物科学知识涉及实验测试假设和解释基于先前的科学工作的结果。研究人员面临的挑战是收集、评估和整合大量关于生物体、细胞、基因和蛋白质的不同类型的信息，以生成有效的假设。一旦产生了一个假设，挑战就在于根据已知的情况来评估这个假设。我们拟议的研究将：1。测试一种新型计算机系统的可扩展性和可扩展性，该系统允许生物学家构建和评估基于酵母酿酒酵母知识库的替代假设。2.测试支持存档和搜索已验证假设的知识档案。 在我们的工作中，我们将假设定义为关于生物系统组件之间关系的声明，旨在解释实验观察。一组经过验证的假设可以用作构建更大，更复杂的模型，如途径。我们计划开发和测试一个新的范例：假设驱动查询模式生物知识库。这项工作被称为HyQue（基于假设的路径模型查询），将以基于知识的形式主义表达的关于路径模型的工作假设作为输入，使用知识库中的现有数据评估它们的一致性，并作为输出提供矛盾的证据和改进假设的建议。 HyQue将结合基于语义网标准的正式知识表示和一个本体来表示生物对象和关系。HyQue还将包含一个规则库，用于确定给定假设的支持和矛盾计数。我们将建立一个假设档案的原型，允许用户将他们的假设与同行提交的其他假设进行比较。 我们将探索以下能力：（1）表达关于酵母细胞周期的工作假设;（2）提供酵母基因组数据的整合 用于评估/测试特定路径假设的数据库; （3）将这些结果存档。随着分析工具和数据库资源的激增，生物学家需要将现有数据整合到知识中的设施，以创建对生物模型的共同理解。我们的工作将探索独特的和新颖的查询和基于矛盾的推理方法，使用丰富的生物事件的形式化知识规范，以完成信息集成的表达性和可扩展性。我们提出的工作将导致一个新的范式查询生物知识，可以动态检索，整合和解释信息的生物相关关系断言为途径模型。我们的工作将探讨语义网技术在建立这样的查询和推理的知识库的价值，并将有助于生物知识的标准化模型，并增加势头的一系列正在进行的本体论建设工作。有关该项目的更多信息，请访问项目网页：http://nigam.web.stanford.edu/hyque