Gene Ontology Consortium and Knowledgebase

项目摘要/摘要 由于生物系统的惊人复杂性，生物医学研究正变得越来越多。 依赖于以可计算形式存储的知识。基因本体论(GO)是迄今为止最大的 基因如何发挥作用的知识库，并已成为计算的关键组件 基础设施使基因组革命成为可能。围棋知识库编码了一个计算模型 使用现代语义技术的生物系统，这是它被广泛采用和 申请。它存储的知识比一个人知道的要多得多，因此能够进行计算 分析，否则是不可能的。它已经成为大尺度解说中不可或缺的一部分 生物学研究中的分子测量。对人类健康研究至关重要的是，围棋也是其中之一 以这种方式构建的互补本体最大限度地促进互操作性和 数据集的可比性。它代表了可以被干扰的基因功能和生物过程。 人类疾病，帮助研究人员或临床医生确定疾病的遗传因素。 围棋是一个可以进行统计挖掘的知识库，它可以是独立的，也可以与来自 其他知识资源，使研究人员能够发现联系并形成新的假设 来自生物网络的GO代表。围棋中的所有知识都用语义网表示 技术等都适用于计算集成和一致性检查。 为了确保知识环境符合生物医学研究人员的要求，我们将：1)发展 并提炼基因本体以反映当前的生物学知识；2)协调、集成和提供围棋 来自多个来源的断言；3)增强GO资源对多个研究社区的可用性。 我们将扩大我们的贡献者联盟的范围，以有效地扩展 知识库，并开发测试集和挑战，以推动机器学习方法的发展 用于获取知识。我们的目标反映了实现以下总体目标的基本要求 生物医学知识库：高效获取和整合生物知识，坚持 精确度和细节的最高标准；构建和提供一个健壮、灵活、强大和 可扩展的技术基础设施不仅可供内部使用，而且更广泛的用户也可以轻松使用 社区；最后，利用最先进的社交媒体、网络服务和其他技术 将GO资源分发给整个生物医学研究社区。

Project Summary/Abstract Because of the staggering complexity of biological systems, biomedical research is becoming increasingly dependent on knowledge stored in a computable form. The Gene Ontology (GO) is by far the largest knowledgebase of how genes function, and has become a critical component of the computational infrastructure enabling the genomic revolution. The GO knowledgebase encodes a computational model of biological systems using modern semantic technologies, and this is the key to its broad adoption and application. It stores vastly more knowledge than one person can know, and therefore enables computational analyses that would otherwise be impossible. It has become indispensable in the interpretation of large-scale molecular measurements in biological research. Crucially for human health research, GO is also one of a suite of complementary ontologies constructed in such a way to maximally promote interoperability and comparability of data sets. It represents the gene functions and biological processes that can be perturbed in human disease, helping researchers or clinicians to identify genetic contributions to disease. GO is a knowledgebase that can be statistically mined, either standalone or in combination with data from other knowledge resources, which enables researchers to discover connections and form new hypotheses from the biological networks GO represents. All knowledge in GO is represented using semantic web technologies and so is amenable to computational integration and consistency checking. To ensure the knowledge environment meets the requirements of biomedical researchers, we will: 1) Develop and refine the Gene Ontology to reflect current biological knowledge; 2) Coordinate, integrate, and provide GO assertions from multiple sources; 3) Enhance usability of the GO resources for multiple research communities. We will extend the reach of our Consortium of contributors, to efficiently expand the content of the knowledgebase, and develop test sets and challenges to spur the development of machine learning methods for knowledge capture. Our aims reflect the essential requirements for realizing the overarching objectives for a biomedical knowledgebase: efficiently capturing and integrating biological knowledge and adhering to the highest possible standard for accuracy and detail; constructing and providing a robust, flexible, powerful, and extensible technological infrastructure available not only for internal use but just as easily by the wider community; and lastly, leveraging state-of-the-art social media, web services and other technologies to disseminate the GO resource to the entire biomedical research community.