SGER: Next Generation Computer-Assisted Thinking Tools for Plant Scientists.

SGER：植物科学家的下一代计算机辅助思维工具。

• 批准号: 0531868
• 负责人: Nina Fedoroff
• 金额: --
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-12-01 至 2007-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0531868&HistoricalAwards=false
• 关键词: SGER; Next; Generation; Computer; Assisted

Contemporary experimental biologists have access to a large body of disparate kinds of information about many different organisms and biological systems. Although much of the information is still in papers published in journals, primary data increasingly reside in electronically accessible databases. The volume and complexity of the available data exceed the synthetic and reasoning capacities of any individual researcher, stimulating the development of knowledge bases, which represent information about biological systems at a higher level of abstraction than do databases. A serious limitation on the usefulness of existing knowledge bases is that knowledge about the system is effectively "frozen" as it is archived. Observations that contradict the bulk of available evidence are generally omitted at the time of annotation. However, a deeper understanding of how biological systems operate often begins with an observation that contradicts existing knowledge. The overall objective of this project is to create computational tools that allow the experimental biologist to explore accumulated information about biological systems without precluding access to contradictory information. The approach differs markedly from those currently used to construct knowledge bases, such as the pathway databases Reactome (www.reactome.org) and BioCyc (www.biocyc.org), which store currently accepted models based on expert input and literature information and they must be revised and rewritten as the knowledge grows. The approach being taken here is to instead store the ingredients for model building at the evidence level and enable biologists to continuously and actively participate in model- building through the familiar device of formulating and testing hypotheses. The hypotheses themselves are used to query the stored data by breaking each hypothesis down into its constituent relationships and extracting all of the explicit and implicit assertions that must hold in order for the hypothesis to be valid. A set of evaluation rules is applied to test these assertions for agreement with different types of data and present the user with links to information and data that support the hypothesis, as well as links to those that contradict it. Thus, static conclusions are not archived, but instead the data required are stored to elucidate relationships that exist in the system. Although the experimenter's ideas about relationships are tested against what is known, conclusions are not imposed. Rather, both supporting information and contradictions are reported and the task of evaluating the weight and significance of each left to the experimenter. This work has several broader impacts. The importance of this approach is that it is a "thinking" tool for the experimental biologist, as opposed to a knowledge base, which is essentially an electronic textbook. Few computer assisted thinking tools are available at present and the success of this project could revolutionize how experimental biologists work. The tools will be integrated into the operation of The Arabidopsis Information Resource (TAIR: www.arabidopsis.org), a community database with 14,000 regular users world-wide.

当代实验生物学家可以获得关于许多不同生物体和生物系统的大量不同种类的信息。尽管许多信息仍在期刊上发表的论文中，但原始数据越来越多地存储在可电子访问的数据库中。现有数据的数量和复杂性超过了任何研究人员的综合和推理能力，刺激了知识库的发展，知识库在比数据库更高的抽象水平上代表了生物系统的信息。现有知识库有用性的一个严重限制是，有关该系统的知识在存档时实际上被“冻结”。与大量现有证据相矛盾的观察结果通常在注解时被省略。然而，对生物系统如何运作的更深层次的理解往往始于与现有知识相矛盾的观察。该项目的总体目标是创建计算工具，允许实验生物学家探索积累的关于生物系统的信息，而不排除获取相互矛盾的信息。这种方法与目前用于构建知识库的方法有很大的不同，例如途径数据库Reactome(www.reactome.org)和BioCyc(www.bicle c.org)，后者存储了当前接受的基于专家输入和文献信息的模型，随着知识的增长，这些模型必须进行修订和重写。这里所采取的方法是将用于建模的成分存储在证据层面，并使生物学家能够通过常见的制定和测试假说的手段来持续和积极地参与模型构建。假设本身用于查询存储的数据，方法是将每个假设分解为其构成关系，并提取为使假设有效而必须持有的所有显式和隐式断言。应用一组评估规则来测试这些断言与不同类型数据的一致性，并向用户呈现指向支持该假设的信息和数据的链接以及指向与该假设相矛盾的信息和数据的链接。因此，静态结论没有存档，而是存储了所需的数据，以阐明系统中存在的关系。尽管实验者关于关系的想法会与已知的东西进行对比，但结论并不会强加于人。相反，支持信息和矛盾都会被报告，评估每一种信息的权重和意义的任务都留给了实验者。这项工作有几个更广泛的影响。这种方法的重要性在于，它是实验生物学家的“思考”工具，而不是知识库，后者本质上是一本电子教科书。目前几乎没有可用的计算机辅助思维工具，这个项目的成功可能会彻底改变实验生物学家的工作方式。这些工具将纳入拟南芥信息资源(tair：www.arabidopsis.org)的运作，这是一个在全世界拥有14 000名经常用户的社区数据库。