Gene Ontology Consortium and Knowledgebase

基因本体联盟和知识库

• 批准号: 10348001
• 负责人: J. Michael Cherry
• 金额: $ 260.39万
• 依托单位: UNIVERSITY OF SOUTHERN CALIFORNIA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-01 至 2027-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10348001
• 关键词: Adoption; Area; Automation; Basic Science; Bioinformatics; Biological; Biological Process; Biological Sciences; Biology; Biomedical Research; Communities; Complex; Computer Analysis; Computer Models; Computers; Data; Data Set; Databases; Development; Disease; Ensure; Environment; Foundations; Gene Family; Genes; Genetic; Genomics; Goals; Health; Human; Human Biology; Improve Access; Incentives; Information Resources; Infrastructure; Ingestion; Knowledge; Link; Machine Learning; Measurement; Medical Research; Modeling; Modernization; Natural Language Processing; Ontology; Pathway Analysis; Pathway interactions; Pattern; Persons; Play; Procedures; Process; Provider; Reproducibility; Research; Research Design; Research Personnel; Resources; Rhea; Role; Semantics; Source; Standardization; Structure; Techniques; Technology; Testing; Training; Update; Work; big biomedical data; biological research; biological systems; biomedical ontology; computational reasoning; computer based Semantic Analysis; computer infrastructure; computer program; computing resources; design; evidence base; experimental study; flexibility; gene function; gene network; genome resource; genomic data; human disease; improved; insight; interoperability; knowledgebase; learning community; machine learning method; meetings; molecular scale; ontology development; outreach; social media; text searching; tool; usability; web services

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.

项目概要/摘要 由于生物系统极其复杂，生物医学研究变得越来越重要 依赖于以可计算形式存储的知识。基因本体（GO）是迄今为止最大的 基因如何发挥作用的知识库，已成为计算的重要组成部分 推动基因组革命的基础设施。 GO 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，这是其广泛采用和应用的关键 应用。它存储的知识远远多于一个人所能知道的知识，因此可以进行计算 进行分析，否则这是不可能的。它已成为大尺度解读中不可或缺的一部分。 生物学研究中的分子测量。对于人类健康研究至关重要，GO 也是其中之一 以最大限度地促进互操作性和 数据集的可比性。它代表了可能受到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。 GO 是一个可以进行统计挖掘的知识库，可以单独进行统计挖掘，也可以与来自以下数据的数据相结合进行统计挖掘： 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 GO 所代表的生物网络。 GO 中的所有知识均使用语义网表示 技术等易于计算集成和一致性检查。 为确保知识环境满足生物医学研究人员的要求，我们将： 1）开发 并完善基因本体以反映当前的生物学知识； 2）协调、整合和提供GO 来自多个来源的断言； 3) 增强 GO 资源对多个研究团体的可用性。 我们将扩大贡献者联盟的影响范围，以有效地扩展内容 知识库，并开发测试集和挑战以刺激机器学习方法的发展 用于知识捕获。我们的目标反映了实现总体目标的基本要求 生物医学知识库：高效捕捉和整合生物知识，遵循 准确性和细节的最高标准；构建并提供一个健壮的、灵活的、强大的、 可扩展的技术基础设施不仅可供内部使用，而且也很容易被更广泛的人使用 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 资源传播给整个生物医学研究界。