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Related Experiment Video

Updated: Jul 18, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

Large-Scale Biomedical Relation Extraction Across Diverse Relation Types: Model Development and Usability Study on

Zeyu Zhang^1,2, Meng Fang³, Rebecca Wu⁴

¹Research Center for Translational Medicine, Shanghai East Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, China.

Journal of Medical Internet Research

|August 26, 2023

Summary

Biomedical relation extraction (RE) models were optimized for diverse relation types, improving knowledge discovery and clinical applications. Enhanced RE tools and models, including a COVID-19 relation graph, are now publicly available for researchers.

Keywords:

COVID-19 biomedical relation extraction biomedical text mining clinical drug path knowledge discovery knowledge graph pretrained language model task-adaptive pretraining

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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

Area of Science:

Biomedical Informatics
Natural Language Processing
Computational Biology

Background:

Biomedical relation extraction (RE) is crucial for systematic studies, knowledge mining (e.g., knowledge graphs), and translational applications (e.g., clinical diagnosis, precision medicine).
Current biomedical RE faces challenges due to the complexity and diversity of entity relations, with comprehensive systems not yet well-established.

Purpose of the Study:

To investigate and enhance large-scale biomedical relation extraction (RE) across diverse relation types.
To conduct usability studies with practical application scenarios to optimize biomedical text mining.

Main Methods:

Constructed datasets with 125 relation types and varying entity semantic levels to assess the impact of semantic information on RE performance.
Performed performance analysis on different model architectures and domain-specific models, proposing a continued pretraining strategy.
Applied RE to the COVID-19 corpus, evaluating biological interpretability and usability in clinical contexts.

Main Results:

RE performance is maximized with detailed semantic type information.
PubMedBERT with continued pretraining achieved the highest F1-score of 0.8998 for a single model.
Usability studies on COVID-19 data demonstrated RE's interpretability and utility, leading to a relation graph database revealing drug paths.

Conclusions:

This study offers a comprehensive analysis and optimization of RE for diverse biomedical relation types, yielding improved models and tools for text mining.
The developed RE models and integrated tool (Docker) are publicly available, supporting the biomedical text mining community and clinical researchers.
Usability studies validated the potential of large-scale RE for facilitating novel biomedical research and discovery.