Stanford Center for Biomedical Informatics Research, Stanford University, 3180 Porter Dr, Palo Alto, CA 94304, United States.
Center for Computational Biomedicine, Harvard Medical School, 10 Shattuck St, Boston, MA 02115, United States.
Database (Oxford). 2024 Nov 28;2024. doi: 10.1093/database/baae119.
There is an ongoing need for scalable tools to aid researchers in both retrospective and prospective standardization of discrete entity types-such as disease names, cell types, or chemicals-that are used in metadata associated with biomedical data. When metadata are not well-structured or precise, the associated data are harder to find and are often burdensome to reuse, analyze, or integrate with other datasets due to the upfront curation effort required to make the data usable-typically through retrospective standardization and cleaning of the (meta)data. With the goal of facilitating the task of standardizing metadata-either in bulk or in a one-by-one fashion, e.g. to support autocompletion of biomedical entities in forms-we have developed an open-source tool called text2term that maps free-text descriptions of biomedical entities to controlled terms in ontologies. The tool is highly configurable and can be used in multiple ways that cater to different users and expertise levels-it is available on Python Package Index and can be used programmatically as any Python package; it can also be used via a command-line interface or via our hosted, graphical user interface-based web application or by deploying a local instance of our interactive application using Docker. Database URL: https://pypi.org/project/text2term.
目前需要可扩展的工具来帮助研究人员对离散实体类型（例如疾病名称、细胞类型或化学物质）进行回溯和前瞻性标准化，这些实体类型用于与生物医学数据相关的元数据中。如果元数据结构不良或不够精确，那么相关数据就更难找到，并且由于需要进行前期整理工作才能使数据可用（通常是通过回溯标准化和清理（元）数据），因此数据通常难以重用、分析或与其他数据集集成。为了方便元数据标准化的任务（无论是批量进行还是逐个进行，例如支持在表单中自动补全生物医学实体），我们开发了一个名为 text2term 的开源工具，该工具可将生物医学实体的自由文本描述映射到本体中的受控术语。该工具具有高度可配置性，可以通过多种方式使用，以满足不同用户和专业水平的需求——它在 Python 包索引上可用，可以像任何 Python 包一样通过编程使用；也可以通过命令行界面或我们基于图形用户界面的托管网络应用程序使用，或者通过使用 Docker 部署我们的交互式应用程序的本地实例使用。数据库 URL：https://pypi.org/project/text2term。
