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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 Learning in Bioinformatics: A Survey of Graph Neural Network Architectures, Biological Graph Construction and

Lijia Deng¹, Ziyang Dong², Zhengling Yang³

¹Clinical Medical Research Center, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu 610054, China.

|February 27, 2026

Summary

Graph Neural Networks (GNNs) offer powerful tools for analyzing complex biological data. This review provides a framework for applying GNNs in bioinformatics, covering graph construction, architectures, and applications.

Keywords:

bioinformatics biological graph construction graph convolutional networks graph neural network

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Area of Science:

Computational Biology
Bioinformatics
Machine Learning

Background:

Biological systems data, including protein interaction networks and multi-omics data, possess inherent non-Euclidean structures.
Graph Neural Networks (GNNs) excel at modeling these relational biological datasets, capturing complex dependencies missed by traditional methods.
The efficacy of GNNs in bioinformatics is contingent upon graph construction, parameterization, and training strategies.

Purpose of the Study:

To present a structured framework for understanding and applying GNNs in bioinformatics.
To consolidate knowledge on graph construction, GNN architectures, and their applications in biomedical research.
To examine critical training considerations and emerging challenges in the field.

Main Methods:

A comprehensive review of GNN methodologies in bioinformatics.
Categorization of GNNs based on graph construction and representation, architectural formulations (spectral/spatial), and specific models (GCNs, GATs, GraphSAGE, GIN).
Exploration of applications in disease-gene association, drug discovery, protein analysis, multi-omics integration, and biomedical knowledge graphs, alongside training considerations.

Main Results:

GNNs are highly effective for diverse bioinformatics tasks due to their ability to model relational data.
The review synthesizes methodological foundations and domain-specific applications, clarifying the interplay between graph quality, architecture, and training dynamics.
Key GNN architectures and their suitability for different biological data types are discussed.

Conclusions:

Effective application of GNNs in bioinformatics requires careful consideration of graph construction, model architecture, and training strategies.
Emerging challenges include modeling temporal processes, enhancing interpretability, and multimodal data fusion.
This review provides a roadmap for leveraging GNNs in computational biology and highlights future research directions.