文本2术语工具将生物医学术语的自由文本描述映射到本体学
Rafael S Gonçalves1, Jason Payne2, Amelia Tan3
1Stanford Center for Biomedical Informatics Research, Stanford University, 3180 Porter Dr, Palo Alto, CA 94304, United States.
Database : the journal of biological databases and curation
|November 28, 2024
概括
研究人员现在可以通过text2term更轻松地标准化生物医学元数据,这是一个开源工具,将自由文本实体描述映射到本体学术语中. 这提高了数据的可发现性和可重复使用性.
科学领域:
- 生物医学信息学 生物医学信息学
- 数据策划数据策划
- 超级数据标准化标准化
背景情况:
- 生物医学数据的重复使用,分析和整合受到结构不良或不准确的元数据的阻碍.
- 在元数据中缺乏标准化的离散实体类型 (例如疾病,细胞类型,化学物质) 需要大量的预先策划努力.
- 对于这些生物医学实体的回顾性和前性标准化,需要可扩展的工具.
研究的目的:
- 开发一个开源工具,text2term,以促进生物医学元数据的标准化.
- 为了使生物医学实体的自由文本描述能够映射到本体学中的受控术语.
- 支持批量和逐一标准化,包括自动完成功能.
主要方法:
- 开发了text2term,这是一个在Python包索引上可用的开源工具.
- 该工具将自由文本生物医学实体描述映射到基于本体学的受控术语.
- text2term提供多种使用模式:编程API,命令行界面,托管图形Web应用程序和Docker部署.
主要成果:
- text2term有效地将自由文本生物医学实体映射到标准化的本体学术语.
- 该工具高度可配置,满足各种用户专业知识水平和用例.
- 多个接入点 (API,CLI,Web应用程序,Docker) 提高了工具的可访问性和采用性.
结论:
- text2term为生物医学元数据标准化提供了一个可扩展和多功能解决方案.
- 该工具解决了通过精确元数据改进数据可发现性,可重复使用性和整合性的需求.
- text2term通过简化生物医学实体的策划和标准化来赋予研究人员权力.
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