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

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BioRel: towards large-scale biomedical relation extraction.

Rui Xing¹, Jie Luo², Tengwei Song¹

¹State Key Laboratory of Software Development Environment, School of Computer Science and Engineering, Beihang University, No. 37 Xueyuan Road, Haidian District, Beijing, 100191, China.

BMC Bioinformatics

|December 16, 2020

Summary

This summary is machine-generated.

Researchers created BioRel, a large-scale dataset for biomedical relation extraction, addressing limitations of human-annotated data. This dataset enables development and evaluation of advanced machine learning models for extracting structured knowledge from biomedical literature.

Keywords:

Distant supervision Information extraction Medline Relation extraction

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Area of Science:

Biomedical Informatics
Natural Language Processing
Machine Learning

Background:

Biomedical literature is rapidly expanding, but lacks structured, machine-readable knowledge.
Relation extraction from plain text is crucial for transforming unstructured data into usable formats.
Existing biomedical relation extraction datasets are often human-annotated, limiting their scale and efficiency.

Purpose of the Study:

To develop a large-scale dataset for biomedical relation extraction.
To overcome the limitations of labor-intensive and time-consuming human annotation processes.
To facilitate the advancement of automated knowledge extraction from biomedical texts.

Main Methods:

Constructed the BioRel dataset using the Unified Medical Language System (UMLS) as a knowledge base and MEDLINE as the corpus.
Utilized MetaMap to identify and link entity mentions in MEDLINE sentences to UMLS.
Applied distant supervision to assign relation labels to sentences.
Adapted state-of-the-art deep learning and statistical machine learning methods as baseline models.

Main Results:

Developed BioRel, a large-scale dataset for biomedical relation extraction.
Established baseline performance metrics using adapted deep learning and statistical machine learning models.
Demonstrated the dataset's utility for evaluating relation extraction techniques.

Conclusions:

BioRel is a suitable large-scale dataset for biomedical relation extraction.
The dataset provides reasonable baseline performance for evaluating models.
BioRel presents remaining challenges and opportunities for both deep learning and statistical methods in biomedical NLP.