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Related Experiment Video

Updated: Dec 13, 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 entity representation learning for biomedical relationship extraction.

Mario Sänger¹, Ulf Leser¹

¹Computer Science Department, Knowledge Management in Bioinformatics, Humboldt-Universität zu Berlin, Berlin 10099, Germany.

Bioinformatics (Oxford, England)

|July 30, 2020

Summary

This summary is machine-generated.

This study introduces novel relation extraction methods using entity embeddings learned from all PubMed publications. These corpus-based approaches improve the accuracy of identifying biomedical relationships compared to traditional sentence- or article-based methods.

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Last Updated: Dec 13, 2025

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

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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
Computational Biology
Natural Language Processing

Background:

Extracting relationships between molecular entities is crucial for systems biology and personalized medicine.
Existing methods often rely on single articles or sentences, which may lack sufficient evidence or context.
Conflicting information across publications necessitates a comprehensive literature-based approach for reliable relationship assessment.

Purpose of the Study:

To develop novel, corpus-based relation extraction approaches for biomedical entities.
To leverage representation learning for comprehensive entity and entity-pair modeling.
To improve the accuracy and reliability of automated biomedical relationship extraction.

Main Methods:

Proposed two novel relation extraction approaches utilizing representation learning.
Learned entity and entity-pair representations by considering all PubMed publications mentioning them.
Employed a neural network for global relation classification using learned embeddings, moving beyond sentence- or article-level analysis.

Main Results:

Learned embeddings effectively capture semantic information of biomedical entities.
Outperformed traditional methods by 4-29% in F1 score for mutation-disease, drug-disease, and drug-drug relationship extraction.
Demonstrated the effectiveness of corpus-based, embedding-driven relation classification.

Conclusions:

The proposed methods provide a more robust and accurate way to extract biomedical relationships by integrating information from the entire literature.
Representation learning offers a powerful tool for building comprehensive models of biomedical entities.
These advancements contribute to more reliable automated analysis in fields like systems biology and personalized medicine.