Unifying Templates, Ontologies and Tools to Achieve Effective Annotation of Bioassay Protocols

项目摘要 生物检测是开发化学探针和药物的基础，但新的大数据方法 -已经彻底改变了生物医学科学的其他领域-还没有推进这一早期的步骤 生物医学研究：化验数据分析。障碍是科学家通过文字说明他们的化验结果 用科技英语编写的描述，需要翻译成标准化的注释，可供 电脑。缺乏标准化和机器可读的化验说明是一个主要障碍 管理、查找、汇总、比较、重复使用和学习不断增长的分析语料库(例如，&gt；1.2 百万美元的PubChem)。因此，迫切需要更好的注释和整理工具来进行药物发现 化验。然而，从简单的文本协议到高度详细的机器可读语义的过程 注释并不是微不足道的。需要多种工具和技术：本体或结构化受控 词汇表；将特定词汇表映射到要捕获的属性的模板；以及软件工具 将这些本体论实际应用于给定的文本。目前，这些都是孤立存在的；然而，它们都是一个瓶颈 在任何一种工具或技术中，或者不同部件之间的差距，都会扰乱整个过程，导致 数据集的注释很差或没有注释。在这里，我们提出了一个项目，将这三者结合起来 技术(这也是就该提案开展合作的三个小组的核心能力)。我们 将提供一个新颖、全面、用户友好的数据注释和管理系统，该系统高度 相互关联，包括所有人所需任务和决策的整个周期和现实世界的实践 生物测定注释生态系统内的缔约方(执行策展的研究人员、专门的策展人、IT 专家、本体论所有者和图书馆员/存储库)。学术与商业的联盟 已经在一起工作的合作者将极大地使项目受益，并将执行风险降至最低。我们的 具体目标是：(1)采用斯坦福大学的方法开发生物检测专用模板编辑器和模板 (扩展数据注释和检索中心，Cedar)基于机器学习的数据模型 管理工具生物分析Express，以开发其数据结构、工具和界面的广泛功能；(2) 定义并创建本体更新流程和工具(`OntoloBridge‘)，以支持 馆长/用户和本体论专家，并支持半自动合并更新建议 现有已发布的本体；(3)开发新的工具将带注释的数据导出到公共存储库，例如 PubChem；以及(4)在不同的受众(制药、学术界、仓库)中评估我们的解决方案。这个 系统将提高生物检测管理的效率、质量和效果，使科学家能够生成 为他们的实验提供标准化注释，以使这些数据公平(可查找、可访问、可互操作、 可重复使用)。我们设想这套工具将鼓励在数据生命周期的早期进行注释，同时仍然 支持后续阶段的注释(例如，提交给储存库或期刊)。

Project Summary Biological assays are the foundation for developing chemical probes and drugs, but new Big Data approaches – which have revolutionized other areas of biomedical science – have not yet advanced this early step of biomedical research: analysis of assay data. The obstacle is that scientists specify their assays through text descriptions written in scientific English, which need to be translated into standardized annotations readable by computers. This lack of standardized and machine-readable assay descriptions is a major impediment to manage, find, aggregate, compare, re-use, and learn from the ever-growing corpus of assays (e.g., >1.2 million in PubChem). Thus, there is a critical need for better annotation and curation tools for drug discovery assays. However, the process to go from a simple text protocol to highly detailed machine-readable semantic annotations is not trivial. Multiple tools and technologies are required: ontologies or the structured controlled vocabularies; templates that map specific vocabularies to properties that are to be captured; and software tools to actually apply these ontologies to a given text. Currently, each of these exists in isolation; yet, a bottleneck in any one tool or technology, or a gap between the different pieces, disrupts the overall process, resulting in poor or no annotation of the datasets. Here we propose a project to combine and integrate these three technologies (which are also the core competencies of the three groups collaborating on this proposal). We will deliver a novel, comprehensive, user-friendly data annotation and curation system that is highly interconnected, encompassing the full cycle, and real-world practice, of required tasks and decisions, by all parties within the `bioassay annotation ecosystem' (researchers performing curation, dedicated curators, IT specialists, ontology owners, and librarians/repositories). The alliance between academic and commercial collaborators, who already work together, will greatly benefit the project and minimize execution risk. Our specific aims are to: (1) Develop a bioassay-specific template editor and templates by adopting the Stanford (Center for Expanded Data Annotation and Retrieval, CEDAR) data model to the machine learning-based curation tool BioAssay Express, to exploit the broad functionality of its data structures, tools and interfaces; (2) Define and create an ontology update process and tool (`OntoloBridge') to support rapid feedback between curators/users and ontology experts and enable semi-automated incorporation of suggestions for updates to existing published ontologies; (3) Develop new tools to export annotated data into public repositories such as PubChem; and (4) Evaluate our solution across diverse audiences (pharma, academia, repositories). The system will improve bioassay curation efficiency, quality, and effectiveness, enabling scientists to generate standardized annotations for their experiments to make these data FAIR (Findable, Accessible, Interoperable, Reusable). We envision this suite of tools will encourage annotation earlier in the data lifecycle while still supporting annotation at later stages (e.g., submission to repositories or to journals).