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

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DeepKG: an end-to-end deep learning-based workflow for biomedical knowledge graph extraction, optimization and

Zongren Li^1,2, Qin Zhong³, Jing Yang³

¹Medical Big Data Research Center, Chinese PLA General Hospital, Beijing 100039, China.

Bioinformatics (Oxford, England)

|November 17, 2021

Summary

This summary is machine-generated.

DeepKG is a deep learning workflow that extracts knowledge from biomedical literature to build custom knowledge graphs. This aids in understanding diseases and drug repurposing, with results available online.

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Area of Science:

Biomedical Informatics
Artificial Intelligence in Medicine

Background:

Biomedical literature contains vast, unstructured knowledge crucial for research.
Extracting this knowledge efficiently is challenging for researchers.

Purpose of the Study:

To develop an automated workflow (DeepKG) for mining knowledge from biomedical literature.
To enhance knowledge representation and inference for better understanding of disease mechanisms and drug repurposing.

Main Methods:

Implemented a cascaded hybrid information extraction framework for 3-tuple extraction.
Developed a novel AutoML-based knowledge representation algorithm (AutoTransX).
Applied DeepKG to 144,900 COVID-19 literature articles.

Main Results:

Generated a high-quality knowledge graph with 7980 entities and 43,760 3-tuples from COVID-19 literature.
Identified a candidate drug list and relevant animal experimental studies.
DeepKG has been deployed in hospitals, demonstrating effectiveness.

Conclusions:

DeepKG provides an effective end-to-end solution for automated knowledge discovery in biomedical research.
The publicly available DeepKG tool accelerates research in areas like drug repurposing and clinical studies.