UniProt: A Protein Sequence and Function Resource for Biomedical Science

项目摘要/摘要 该项目延续了UniProt知识库的发展，旨在提供科学的 社区拥有全面、高质量且可免费访问的蛋白质序列和 功能信息。蛋白质是连接人类遗传、环境和环境的重要桥梁 表型。虽然人类遗传学在发现基因型和表型之间的相关性方面能力越来越强， 由UniProt提供的蛋白质功能知识对于从机制上理解至关重要的 通过改进和个性化的诊断、预后和治疗来改善健康结果。 来自人工智能领域的方法正在给生物医学研究带来革命性的变化，特别是 机器学习(ML)方法，如深度学习(DL)。这些方法现在超过了 当有足够的数据可用时，人类在许多领域都是最先进的。UniProt提供黄金标准 生物医学研究中数百个ML应用程序的训练数据。这项建议中的工作将加强 UniProt已准备好在ML中使用，并将整合ML方法以提高我们的效率。 UniProt策展人从人体和机器的论文中提取和合成蛋白质的实验知识- 使用一系列标准本体的可读表单。这项建议将进一步构建蛋白质知识在 UniProt，开发完整的、机器可读的人类变异的功能影响目录 人类蛋白质网络和复合体，对了解人类疾病至关重要。管理的效率将 使用与文本挖掘专家合作开发的DL模型进行改进，以自动进行识别 获取相关论文，加快知识提取。这一提取的知识将由我们的 专家馆长以及更广泛的研究界，他们将积极参与进一步扩大馆藏规模。 ML方法也将被用于推断没有实验表征的蛋白质的注释，使用 社区面临的挑战是开发更快、更准确、可扩展的方法来注释泛滥的 未鉴定的蛋白质。 UniProt是一个模范的公平资源，已经为科学界提供了节拍数据发布 尽管数据量呈指数级增长。简化的生产流程将高效地进行扩展， 可持续地应对不断增长的数据量和复杂性。我们将探索新技术，以确保 继续按照公平原则及时向社会发布数据。 UniProt是一个国际蛋白质数据中心，每年为数十万用户提供服务。我们会 继续使用以用户为中心的方法开发UniProt网站，以满足用户需求和新数据 类型。我们将通过引入年度战略合作伙伴关系来吸引我们的利益相关者和合作者 开会。我们将通过网络研讨会、社交媒体、黑客松和参加 科学会议，以扩大我们的数据的有效和有影响力的使用。

PROJECT SUMMARY/ABSTRACT This project continues the development of the UniProt Knowledgebase, which aims to provide the scientific community with a comprehensive, high-quality, and freely accessible resource of protein sequences and functional information. Proteins are an essential bridge between human genetics, the environment and phenotype. While human genetics has increasing power to find correlations between genotype and phenotype, knowledge of how proteins function, provided by UniProt, is essential for the mechanistic understanding critical to develop health outcomes through improved and personalized diagnostics, prognostics, and treatments. Biomedical research is being revolutionized by methods from the field of Artificial Intelligence, particularly Machine Learning (ML) approaches such as Deep Learning (DL). These approaches now outstrip the ability of humans in many fields and are state-of-the-art when sufficient data is available. UniProt provides gold standard training data for hundreds of ML applications in biomedical research. The work in this proposal will enhance the readiness of UniProt for use in ML and will integrate ML methods to enhance our efficiency. UniProt curators extract and synthesize experimental knowledge of proteins from papers in human and machine- readable forms using a range of standard ontologies. This proposal will further structure protein knowledge in UniProt, developing complete, machine-readable catalogs of the functional impact of human variation and of human protein networks and complexes, essential to understanding human disease. Efficiency of curation will be improved using DL models, developed in collaboration with text mining experts, to automate the identification of relevant papers and accelerate extraction of knowledge. This extracted knowledge will be validated by our expert curators and also the wider research community who will be actively engaged to further scale curation. ML approaches will also be used to infer annotations for proteins with no experimental characterization, using community challenges to develop faster, more accurate, scalable approaches to annotate the deluge of uncharacterized proteins. UniProt is an exemplar FAIR resource and has served the scientific community with metronomic data releases despite an exponential growth in data volumes. Streamlined production processes will scale efficiently and sustainably with both the growing data volume and complexity. We will explore novel technologies to ensure the continued timely release of data to the community according to the FAIR principles. UniProt is an international hub of protein data that serves hundreds of thousands of users annually. We will continue using user-centric approaches to develop the UniProt website in response to user needs and new data types. We will engage with our stakeholders and collaborators by introducing an annual strategic partnership meeting. We will engage our communities through webinars, social media, hackathons and attendance at scientific meetings to broaden the efficient and impactful use of our data.