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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

A comprehensive review on knowledge graphs for complex diseases.

Yang Yang^1,2, Yuwei Lu^1,2, Wenying Yan³

¹School of Computer Science & Technology, Soochow University, Suzhou 215000, China.

Briefings in Bioinformatics

|December 18, 2022

Summary

This summary is machine-generated.

Knowledge graphs (KGs) are revolutionizing complex disease research by enhancing diagnosis and treatment. This review details KG applications, methods, and future directions in biomedicine.

Keywords:

biomedicine complex disease entity extraction knowledge graphs relation extraction

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

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Area of Science:

Biomedical informatics
Computational biology
Clinical informatics

Background:

Knowledge graphs (KGs) are increasingly utilized for storing entity relationships and enabling advanced reasoning.
In biomedicine, KGs offer a promising approach for efficient and flexible diagnosis and treatment of complex diseases.

Conclusions:

Knowledge graphs represent a significant advancement in managing and reasoning over biomedical data for complex diseases.
The review highlights existing gaps and opportunities, proposing future research directions for KG development in disease diagnosis and treatment.