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.

项目总结/摘要 由于生物系统惊人的复杂性，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 基因如何运作的知识库，并已成为计算的关键组成部分， 基因组革命的基础设施。GO知识库编码了一个计算模型， 生物系统使用现代语义技术，这是其广泛采用的关键， 应用程序.它存储的知识比一个人所能知道的要多得多， 否则不可能进行的分析。它已成为不可或缺的解释大规模 生物研究中的分子测量。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的方式构建互补本体， 数据集的可比性。它代表了基因功能和生物学过程，可以被干扰， 人类疾病，帮助研究人员或临床医生确定疾病的遗传贡献。 GO是一个知识库，可以独立进行统计挖掘，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 从生物网络GO代表。GO中的所有知识都使用语义网表示 技术，因此适合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）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，并提供GO 来自多个来源的断言; 3）增强GO资源对于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，以有效地扩大 知识库，并开发测试集和挑战以刺激机器学习方法的发展 知识捕获。我们的目标反映了实现2010年总体目标的基本要求， 生物医学知识库：有效地获取和整合生物学知识， 最高的精度和细节标准;构建并提供一个坚固、灵活、功能强大， 可扩展的技术基础设施不仅可供内部使用，而且可供更广泛的使用 最后，利用最先进的社交媒体、网络服务和其他技术， 向整个生物医学研究界传播GO资源。