Department of Information Engineering, University of Padova, Padua, Italy.
BMC Bioinformatics. 2024 Mar 14;25(1):112. doi: 10.1186/s12859-024-05730-9.
The constant growth of biomedical data is accompanied by the need for new methodologies to effectively and efficiently extract machine-readable knowledge for training and testing purposes. A crucial aspect in this regard is creating large, often manually or semi-manually, annotated corpora vital for developing effective and efficient methods for tasks like relation extraction, topic recognition, and entity linking. However, manual annotation is expensive and time-consuming especially if not assisted by interactive, intuitive, and collaborative computer-aided tools. To support healthcare experts in the annotation process and foster annotated corpora creation, we present MetaTron. MetaTron is an open-source and free-to-use web-based annotation tool to annotate biomedical data interactively and collaboratively; it supports both mention-level and document-level annotations also integrating automatic built-in predictions. Moreover, MetaTron enables relation annotation with the support of ontologies, functionalities often overlooked by off-the-shelf annotation tools.
We conducted a qualitative analysis to compare MetaTron with a set of manual annotation tools including TeamTat, INCEpTION, LightTag, MedTAG, and brat, on three sets of criteria: technical, data, and functional. A quantitative evaluation allowed us to assess MetaTron performances in terms of time and number of clicks to annotate a set of documents. The results indicated that MetaTron fulfills almost all the selected criteria and achieves the best performances.
MetaTron stands out as one of the few annotation tools targeting the biomedical domain supporting the annotation of relations, and fully customizable with documents in several formats-PDF included, as well as abstracts retrieved from PubMed, Semantic Scholar, and OpenAIRE. To meet any user need, we released MetaTron both as an online instance and as a Docker image locally deployable.
生物医学数据的持续增长伴随着需要新的方法来有效地提取机器可读的知识，以便进行训练和测试。在这方面，一个关键的方面是创建大型的、通常是手动或半自动的标注语料库，这对于开发关系提取、主题识别和实体链接等任务的有效和高效方法至关重要。然而，手动标注非常昂贵且耗时，特别是如果没有交互式、直观和协作的计算机辅助工具的帮助。为了支持医疗保健专家在标注过程中，并促进标注语料库的创建，我们提出了 MetaTron。MetaTron 是一个开源的、免费使用的基于网络的标注工具，用于交互式和协作式地标注生物医学数据；它支持提及级和文档级的标注，同时还集成了自动内置的预测功能。此外，MetaTron 通过支持本体论来实现关系标注，这是许多现成的标注工具经常忽略的功能。
我们进行了定性分析，将 MetaTron 与一组手动标注工具（包括 TeamTat、INCEpTION、LightTag、MedTAG 和 brat）进行了比较，比较的标准有三个：技术、数据和功能。定量评估使我们能够评估 MetaTron 在标注一组文档时的时间和点击次数的性能。结果表明，MetaTron 几乎满足了所有选定的标准，并取得了最佳的性能。
MetaTron 是少数几个针对生物医学领域的标注工具之一，支持关系的标注，并且可以完全定制，支持多种格式的文档，包括 PDF，以及从 PubMed、Semantic Scholar 和 OpenAIRE 检索到的摘要。为了满足任何用户的需求，我们发布了 MetaTron，既可以作为在线实例，也可以作为本地可部署的 Docker 映像。
