The text2term tool to map free-text descriptions of biomedical terms to ontologies

TL;DR

The paper introduces text2term, an open-source, configurable tool that maps free-text biomedical descriptions to ontology terms, aiding metadata standardization and data reuse in biomedical research.

Contribution

It presents a versatile, user-friendly tool that supports bulk and individual mapping of biomedical terms to ontologies, enhancing metadata curation workflows.

Findings

Supports multiple usage modes including Python package, CLI, web app, and Docker deployment.

Facilitates faster and more accurate biomedical metadata standardization.

Open-source and highly configurable for diverse user needs.

Abstract

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; for example, to support auto-completion 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 PyPI 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 (https://text2term.hms.harvard.edu); or by deploying a local instance of our interactive application using Docker.