Identifying key genetic variants in Alzheimer’s disease progression using Graph Convolutional Networks (GCN) and biological impact analysis

Identifying key genetic variants in Alzheimer’s disease progression using Graph Convolutional Networks (GCN) and biological impact analysis

Abstract Alzheimer’s disease (AD) involves complex genetic interactions that remain challenging to model computationally. We present a novel deep learning framework integrating Single Nucleotide Polymorphism (SNP) data with Graph Convolutional Networks (GCNs) to predict gene-disease relationships in...

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Bibliographic Details
Main Authors: Belal A. Hamed, Heba Mamdouh Farghaly, Ahmed Omar, Tarek Abd El-Hafeez
Format: Article
Language:English
Published: SpringerOpen 2025-07-01
Series:Journal of Big Data
Subjects:
Alzheimer's disease
Mutations
Feature extraction
Biological impact
Brain disease
Gene-disease association
Online Access:https://doi.org/10.1186/s40537-025-01228-0
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https://doi.org/10.1186/s40537-025-01228-0

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