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

推进生物科学知识涉及实验检验假设和解释基于先前科学工作的结果。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种类的信息，以产生一个有效的假设。一旦一个假设产生了，挑战就在于如何根据已知的东西来评估这个假设。我们提出的研究将：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根据酵母酵母的知识库构建和评估不同的假设。2. 测试支持已验证假设的存档和搜索的知识存档。在我们的工作中，我们将假设定义为旨在解释实验观察结果的关于生物系统组成部分之间关系的陈述。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）的基石。我们计划开发和测试一种新的范式：模型生物知识库的假设驱动查询。被提议的工作称为HyQue（路径模型的基于假设的查询），将以基于知识的形式表达的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其一致性，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正式知识表示和表示生物对象和关系的本体。HyQue还将包含一个规则库，用于确定给定假设的支持和矛盾数量。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)整合Saccharomyces基因组数据库中的数据，以评估/测试特定途径的假设；(3)存档这些结果。随着分析工具和数据库资源的激增，生物学家需要将现有数据整合到知识中，从而创建对生物模型的共同理解。我们的工作将探索独特和新颖的查询和基于矛盾的推理方法的表达性和可扩展性，这些方法使用丰富的生物事件的形式化知识规范来完成信息集成。我们提出的工作将导致查询生物学知识的新范式，可以动态检索，整合和解释信息的生物学相关关系断言作为途径模型。我们的工作将检验语义网技术在为此类查询和推理建立知识库方面的价值，并将有助于标准化生物知识模型，并为一系列正在进行的本体构建工作增添动力。有关该项目的更多信息可以在该项目的网页上找到：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