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

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Graph representation learning in bioinformatics: trends, methods and applications.

Hai-Cheng Yi^1,2, Zhu-Hong You³, De-Shuang Huang⁴

¹Chinese Academy of Sciences, Xinjiang Technical Institute of Physics and Chemistry, Urumqi 830011, China.

Briefings in Bioinformatics

|September 2, 2021

Summary

This summary is machine-generated.

Graph representation learning embeds complex biomedical graphs into machine learning models. This survey details methods and applications, bridging graph data with bioinformatics for future research.

Keywords:

deep learning graph embedding graph neural network graph representation learning healthcare knowledge graph

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Area of Science:

Bioinformatics
Machine Learning
Graph Theory

Background:

Biomedical data often exists as complex graphs, posing challenges for traditional machine learning.
Graph representation learning addresses this by embedding graph structures into low-dimensional spaces.

Purpose of the Study:

To survey advances in graph representation learning for bioinformatics.
To categorize embedding methods and graph neural networks.
To highlight applications across molecular, genomic, pharmaceutical, and healthcare domains.

Main Methods:

Categorization of graph embedding techniques (homogeneous, heterogeneous, attribute).
Analysis of graph neural network architectures.
Review of applications in diverse biomedical fields.

Main Results:

Comprehensive overview of graph representation learning methods.
Detailed summary of applications from molecular to systems biology.
Identification of open-source platforms and libraries.

Conclusions:

Graph representation learning is crucial for leveraging complex biomedical graph data.
The field offers significant opportunities for future bioinformatics research and development.