VIOLIN 2.0: Vaccine Information and Ontology LInked kNowledgebase

VIOLIN 2.0：疫苗信息和本体链接知识库

• 批准号: 10687095
• 负责人: Yongqun He
• 金额: $ 74.35万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-19 至 2027-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10687095
• 关键词: Acceleration; Acquired Immunodeficiency Syndrome; Address; Adjuvant; Adverse event; Antigens; Applications Grants; Area; Basic Science; Bioinformatics; COVID-19; COVID-19 pandemic; Cessation of life; Clinical; Clinical Trials; Collaborations; Communicable Diseases; Communities; Community Developments; Community Outreach; Consensus; Data; Databases; Detection; Development; Dimensions; Disease; Educational workshop; Ensure; Epidemiology; Evaluation; Event; FAIR principles; Face; Funding; Generations; Grant; HIV; Harvest; Immune; Immune response; Immunologic Factors; Informatics; Information Retrieval; Knowledge; Knowledge Discovery; Knowledge Extraction; Licensing; Life Cycle Stages; Link; Literature; Machine Learning; Malaria; Manuals; Methods; Modern Medicine; Molecular; Monitor; National Institute of Allergy and Infectious Disease; Natural Language Processing; Ontology; Pattern; Public Health; Reproducibility; Research; Resources; Safety; Semantics; Source; Standardization; System; Systems Analysis; Technology; Tuberculosis; United States National Institutes of Health; Vaccination; Vaccine Adjuvant; Vaccine Antigen; Vaccine Design; Vaccines; Visualization; Work; community engagement; data access; data integration; disability; experience; fighting; gene expression database; hackathon; improved; informatics tool; innovation; insight; interoperability; invention; knowledge graph; knowledge integration; knowledgebase; machine learning method; meetings; method development; outreach; pathogen; predictive signature; programs; research and development; response; statistical and machine learning; text searching; tool; vaccine adverse event; vaccine development; vaccine formulation; vaccine safety; vector; web interface; web site; Acceleration; Acquired Immunodeficiency Syndrome; Address; Adjuvant; Adverse event; Antigens; Applications Grants; Area; Basic Science; Bioinformatics; COVID-19; COVID-19 pandemic; Cessation of life; Clinical; Clinical Trials; Collaborations; Communicable Diseases; Communities; Community Developments; Community Outreach; Consensus; Data; Databases; Detection; Development; Dimensions; Disease; Educational workshop; Ensure; Epidemiology; Evaluation; Event; FAIR principles; Face; Funding; Generations; Grant; HIV; Harvest; Immune; Immune response; Immunologic Factors; Informatics; Information Retrieval; Knowledge; Knowledge Discovery; Knowledge Extraction; Licensing; Life Cycle Stages; Link; Literature; Machine Learning; Malaria; Manuals; Methods; Modern Medicine; Molecular; Monitor; National Institute of Allergy and Infectious Disease; Natural Language Processing; Ontology; Pattern; Public Health; Reproducibility; Research; Resources; Safety; Semantics; Source; Standardization; System; Systems Analysis; Technology; Tuberculosis; United States National Institutes of Health; Vaccination; Vaccine Adjuvant; Vaccine Antigen; Vaccine Design; Vaccines; Visualization; Work; community engagement; data access; data integration; disability; experience; fighting; gene expression database; hackathon; improved; informatics tool; innovation; insight; interoperability; invention; knowledge graph; knowledge integration; knowledgebase; machine learning method; meetings; method development; outreach; pathogen; predictive signature; programs; research and development; response; statistical and machine learning; text searching; tool; vaccine adverse event; vaccine development; vaccine formulation; vaccine safety; vector; web interface; web site

Project Summary: Vaccination is one of the most successful innovations in the fight against infectious disease. However, we still lack effective and safe vaccines against many major infectious diseases (e.g., HIV, tuberculosis, and malaria). We also lack a comprehensive and interoperable vaccine knowledgebase to accelerate vaccine development and better understand vaccine safety. Based on the preliminary version of our current VIOLIN vaccine knowledgebase, we propose to develop VIOLIN 2.0, a new generation Vaccine Information and Ontology LInked kNowledgebase. Strong preliminary data were generated: Originally funded by an NIH-NIAID R01, our VIOLIN has grown to include information on >4,000 vaccines for >200 pathogens. In addition, we have led the development of the community-based Vaccine Ontology (VO) and Ontology of Adverse Events (OAE) for vaccine and adverse event representation. We have also developed the widely used Vaxign and Vaxign-ML vaccine design programs and applied them to predict vaccines for many diseases including COVID-19. Many ontology- and bioinformatics-based methods and tools, including natural language processing (NLP) tools, have also been developed to analyze vaccine information and identify new scientific insights. However, the existing VIOLIN also faces new challenges in areas such as knowledge integration, interoperability, and analysis. In this proposal, we aim to systematically develop VIOLIN 2.0, which will be a community-based comprehensive vaccine knowledgebase (KB) with data FAIRness. Basic science, clinical, and public health (safety, epidemiology, vaccine coverage) knowledge will be included with robust linkage and analysis. Four specific aims are proposed: Aim 1: Implement a pipeline for automatic knowledge harvest, standardization, and integration using advanced ontology and natural language processing technologies. Aim 2: Expand the vaccine KB and management. Three specific knowledge aspects will be included: (i) vaccine formulation and development, (ii) protective responses, and (iii) vaccine safety. Aim 3: Provide VIOLIN 2.0 knowledge browser, query, and showcases. For showcase demonstration, three use cases will be built up, including pattern detection of vaccine components (including protective antigens and vaccine adjuvants), vaccine-induced host immune signatures, and vaccine adverse events. The patterns identified will be utilized with statistical and machine learning methods to support rational vaccine design and immune signature prediction. Aim 4: Community engagement and outreach. Many events such as hackathons and workshops will be held to support the development and applications of community-based ontologies, standards, and tools. VIOLIN 2.0 will significantly enhance the VIOLIN with breadth and depth of vaccine information, include knowledge not available in the current VIOLIN (e.g., vaccine adverse events), and develop new methods for efficient and scalable knowledge extraction and analysis. Our study will advance the understanding of vaccine mechanisms, and support rational vaccine design against COVID-19 and other infectious diseases.

项目摘要： 疫苗接种是抗击传染病中最成功的创新之一。但是，我们 仍然缺乏针对许多主要传染病的有效且安全的疫苗（例如，艾滋病毒，结核病和 疟疾）。我们还缺乏全面且可互操作的疫苗知识库来加速疫苗 开发并更好地了解疫苗安全。根据我们当前小提琴的初步版本 疫苗知识库，我们建议开发小提琴2.0，新一代疫苗信息， 本体论链接的知识基础。 生成了强大的初步数据：最初由NIH-NIAID R01资助，我们的小提琴种植了 包括有关> 200个病原体> 4,000疫苗的信息。此外，我们领导了 基于社区的疫苗本体论（VO）和不良事件的本体论（OAE），用于疫苗和不良事件 事件表示。我们还开发了广泛使用的Vaxign和Vaxign-ML疫苗设计 计划并应用它们来预测包括Covid-19在内的许多疾病的疫苗。许多本体学和 基于生物信息学的方法和工具，包括自然语言处理（NLP）工具，也已经是 开发用于分析疫苗信息并确定新的科学见解。但是，现有小提琴 在知识整合，互操作性和分析等领域也面临着新的挑战。 在此提案中，我们旨在系统地开发小提琴2.0，这将是一个基于社区的 具有数据公平性的综合疫苗知识库（KB）。基础科学，临床和公共卫生 （安全性，流行病学，疫苗覆盖范围）知识将包括稳健的联系和分析。四个 提出了具体目的：目标1：实施自动知识收获，标准化， 并使用先进的本体论和自然语言处理技术进行集成。目标2：扩展 疫苗KB和管理。将包括三个特定的知识方面：（i）疫苗配方和 开发，（ii）保护性反应和（iii）疫苗安全。目标3：提供小提琴2.0知识浏览器， 查询和展示。对于展示演示，将建立三个用例，包括模式 检测疫苗成分（包括保护性抗原和疫苗佐剂），疫苗诱导的宿主 免疫特征和疫苗不良事件。确定的模式将用于统计和 支持理性疫苗设计和免疫签名预测的机器学习方法。目标4： 社区参与和宣传。许多活动，例如黑客马拉松和讲习班 支持基于社区的本体论，标准和工具的开发和应用。 小提琴2.0将通过疫苗信息的广度和深度大大增强小提琴，包括 当前小提琴中没有知识（例如疫苗不良事件），并为 有效且可扩展的知识提取和分析。我们的研究将促进对疫苗的理解 机制，并支持针对COVID-19和其他传染病的理性疫苗设计。