The Application of Graph Neural Networks in the Mining of Gene-phenotypic-Drug Networks for Cardiovascular Diseases

DOI: https://doi.org/10.62517/jmhs.202605108

Author(s)

Sichen Yang

Affiliation(s)

Jinqiu International High School, Wuhan, China

Abstract

Cardiovascular diseases, as the leading global health threat, involve a complex interactive network of genetic variations, phenotypic characteristics and drug effects in their pathogenesis. Traditional research methods are limited by the singularity of data dimensions and the concealment of relationships, making it difficult to systematically analyze the multi-level associations of genes, phenotypes, and drugs (G-P-D). Graph Neural Network (GNN) provides a new paradigm for mining G-P-D networks of cardiovascular diseases by integrating non-Euclidean structured data. This article systematically expounds the value of GNN in cardiovascular disease research from three dimensions: theoretical framework, application scenarios, and challenges. It focuses on analyzing its application logic in gene function analysis, phenotypic association discovery, and drug repositioning, providing theoretical support for the construction of a precision medical decision-making system.

Keywords

Graph Neural Network; Cardiovascular Diseases; Gene-Phenotypic - Drug Network; Precision Medicine; Heterogeneous Data Integration

References

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