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

促进生物科学知识的发展包括对假设进行实验测试，并在先前科学工作的基础上解释结果。研究人员面临的挑战是收集、评估和整合关于生物、细胞、基因和蛋白质的大量不同类型的信息，以产生一个有效的假说。一旦产生了一个假说，挑战就是根据已知的情况来评估这个假说。我们提出的研究将：1.测试一个新的计算机系统的可扩展性和可扩展性，该系统允许生物学家根据酿酒酵母的知识库构建和评估替代假设。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，我们将假说定义为关于生物系统组件之间关系的陈述，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(如路径)的构建块。我们计划开发和测试一种新的范式：假设驱动的模式生物知识库查询。这项拟议的工作被称为HyQue(用于基于假设的路径模型查询)，它将以基于知识的形式表示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来评估它们的一致性，并提供相互矛盾的证据和改进假设的建议作为输出。HyQue将结合基于语义网标准的形式化知识表示和本体来表示生物对象和关系。HyQue还将包含一个规则库，用于确定对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他们的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说；(2)提供酵母基因组数据库中的数据集成，以评估/测试特定途径的假说；以及(3)存档这些结果。随着分析工具和数据库资源的激增，生物学家需要设施将现有数据整合到知识中，以建立对生物模型的共同理解。我们的工作将探索独特的、新颖的查询和基于矛盾的推理方法的表现力和可扩展性，这些方法利用丰富的生物事件形式化知识规范来实现信息集成。我们提出的工作将导致一种新的查询生物知识的范例，它可以根据被断言为路径模型的生物相关关系来动态地检索、集成和解释信息。我们的工作将检验语义网技术在为此类查询和推理构建知识库方面的价值，并将有助于生物知识模型的标准化，并为正在进行的一系列本体构建工作增添动力。有关该项目的更多信息，请访问该项目的网页：http://nigam.web.stanford.edu/hyque

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