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February 25, 2026BiomoleculesOpen Access

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

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Authors

LDLijia DengZDZiyang DongZYZhengling Yang

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Overview

This review explores GNN architectures and bioinformatics applications, suggesting new challenges for future research.

Key Points

  • This review aims to delineate the application of graph neural networks in bioinformatics, emphasizing model effectiveness.
  • Structured analysis of graph construction and representation in biological contexts.
  • Examination of various GNN architectures such as GCNs, GATs, and recent advances.
  • Discussion of applications including disease-gene association and protein function analysis.
  • Evaluation of training methodologies and the impact of data quality.
  • Clarification of how graph quality and architectural choice affect GNN model performance.
  • Identification of challenges in modeling temporal processes and enhancing interpretability.
  • Highlighting the necessity for robust multimodal fusion in biological data analysis.

Cite This Study

Deng et al. (2026) studied this question.

synapsesocial.com/papers/699e921bf5123be5ed050167https://doi.org/10.3390/biom16020333
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