Ontology-based Information Network to Support Vaccine Research

项目摘要(摘要)： 自1796年爱德华·詹纳的天花疫苗问世以来，疫苗已被证明对 它们具有刺激免疫系统和保护机体免受病原体侵害的能力。取得的进展 现代疫苗研究伴随着与疫苗相关的疫苗数量的急剧增加 已发表文献中的论文。识别和注释疫苗数据变得越来越具有挑战性 从这一庞大而多样的文献中，没有一个科学家或团队能够完全掌握。虽然疫苗 存在强调商业化疫苗的数据库，没有公共中央存储库可用来存储 关于商业疫苗、临床试验中的疫苗或早期候选疫苗的研究数据 以一种使这些数据可供高级分析使用的方式提供发展的数据。为了满足这一需求，我们有 开发了小提琴(http://www.violinet.org)，)，这是一个基于Web的数据库系统，用于注释、存储和 对已公布的疫苗数据进行分析。本体表示基于共识的受控术语词汇表 和关系，以及相关的定义，这些定义以这样一种方式进行逻辑表述，以促进自动化 推理。疫苗研究和小提琴进一步发展的一个瓶颈是缺乏疫苗本体， 这反过来又为疫苗数据的标准化、检索、集成和高级 分析和预测。我们的目标是开发基于社区的疫苗本体(VO)并将其应用于 高效的疫苗文献挖掘和保护性免疫机制分析。我们将重点介绍两种模式 病原体：大肠埃希氏菌和布鲁氏菌。该项目包含三个具体目标：(1)开发一种 基于社区的疫苗本体(VO)，并将其应用于建立疫苗知识库并推广 通过语义网实现疫苗数据的集成和查询。VO开发将通过以下方式实现 与疫苗研究人员、传染病本体倡议(IDO)和国家 生物医学本体中心(NCBO)；(2)开发基于VO的自然语言处理(NLP)系统 并将其应用于更高效地检索布鲁氏菌和大肠杆菌疫苗信息、自动标注 有VO术语的期刊文章，以及VO的改进。这项任务将通过与 国家综合生物医学信息学中心。(3)分析和预测疫苗目标和 保护性免疫网络可归因于宿主和疫苗之间的相互作用。这将会实现的 主要通过基于VO的文献挖掘和一种新的基于基因组和文献的统计方法。这 该项目将由一个强大的协作团队实施，并得到一个大型用户社区的支持。这个 疫苗本体及其在文献挖掘和布鲁氏菌保护性免疫研究中的应用 SPP.大肠杆菌将为进一步的高级信息学疫苗研究奠定坚实的基础。 后基因组学和信息时代的传染病。

Project Summary (Abstract): Since the introduction of Edward Jenner's smallpox vaccine in 1796, vaccines have proven invaluable for their ability to stimulate the immune system and to confer protection against pathogenic organisms. Progress in modern vaccine research has been accompanied by a dramatic increase in the number of vaccine-related papers in the published literature. It has become increasingly challenging to identify and annotate vaccine data from this large and diverse literature which no one scientist or team can fully master. Although vaccine databases exist that emphasize commercialized vaccines, no public central repository is available to store research data concerning commercial vaccines, vaccines in clinical trials, or vaccine candidates in early stages of development, in a fashion that render such data available for advanced analyses. To fill this need, we have developed VIOLIN (http://www.violinet.org), a web-based database system for annotation, storage, and analysis of published vaccine data. An ontology represents consensus-based controlled vocabularies of terms and relations, with associated definitions which are logically formulated in such a way as to promote automated reasoning. A bottleneck of vaccine research and further VIOLIN development is the lack of a vaccine ontology, which in turn makes a significant obstacle for vaccine data standardization, retrieval, integration, and advanced analysis and prediction. Our goal is to develop the community-based Vaccine Ontology (VO) and apply it to efficient vaccine literature mining and analysis of protective immune mechanisms. We will focus on two model pathogens: Escherichia coli and Brucella species. This project contains three specific aims: (1) develop a community-based Vaccine Ontology (VO), and apply it to establish a vaccine knowledgebase and to promote vaccine data integration and query through Semantic Web. The VO development will be achieved through collaboration with vaccine researchers, the Infectious Disease Ontology (IDO) Initiative, and the National Center for Biomedical Ontology (NCBO); (2) develop a VO-based natural language processing (NLP) system and apply it for more efficient retrieval of Brucella and E. coli vaccine information, automated annotation of journal articles with VO terms, and VO improvement. This task will be achieved by collaboration with the National Center for Integrative Biomedical Informatics (NCIBI). (3) analyze and predict vaccine targets and protective immune networks attributable to the interactions between host and vaccine. This will be achieved mainly by VO-based literature mining and a novel genome- and literature-based statistical methodology. This project will be implemented by a strong collaborative team and supported from a large user community. The Vaccine Ontology and its applications to literature mining and for studying protective immunity against Brucella spp. and E. coli will lay a strong foundation for further advanced informatics research on vaccines against infectious diseases in the post-genomics and information era.