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

项目摘要 生物测定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步骤， 生物医学研究：分析测定数据。障碍在于科学家们通过文本来指定他们的测定方法 用科学英语编写的描述，需要翻译成标准化的注释， 电脑这种缺乏标准化和机器可读的测定描述是实现本发明的主要障碍。 管理、发现、聚集、比较、再使用以及从不断增长的分析语料库（例如，>1.2 PubChem）。因此，迫切需要更好的药物发现注释和管理工具 测定。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 注释不是微不足道的。需要多种工具和技术：本体论或结构化控制 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 to actually其实apply应用these ontologies本体to a given给定text文本.目前，每一个都是孤立存在的;然而， 在任何一个工具或技术，或不同部分之间的差距，扰乱了整个过程，导致 数据集注释不佳或没有注释。在此，我们提出了一个项目，联合收割机和整合这三个 技术（这也是就本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策展系统， 相互关联，包括所有人所需任务和决策的全周期和实际实践， “生物测定注释生态系统”内的各方（执行策展的研究人员、专门的策展人、 专家、本体所有者和图书馆员/存储库）。学术与商业的联盟 已经在一起工作的合作者将大大有利于项目，并将执行风险降至最低。我们 具体目标是：（1）通过采用斯坦福大学 （Center for Expanded Data Annotation and Retrieval，CEDAR）数据模型到基于机器学习的 管理工具BioAssay Express，以利用其数据结构，工具和界面的广泛功能;（2） 定义并创建本体更新过程和工具（“OntoloBridge”），以支持 管理员/用户和本体专家，并能够半自动地合并更新建议， （3）开发新的工具，将带注释的数据导出到公共存储库中， PubChem;以及（4）在不同的受众（制药、学术界、存储库）中评估我们的解决方案。的 该系统将提高生物测定的效率，质量和有效性，使科学家能够产生 标准化的实验注释，使这些数据公平（可查找，可解释，可互操作， 可重复使用）。我们预计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 在稍后阶段支持注释（例如，提交到存储库或期刊）。

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).