DualGCN-GE: integration of spatiotemporal representations from whole-blood expression data with dual-view graph convolution network to identify Parkinson’s disease subtypes

DualGCN-GE: integration of spatiotemporal representations from whole-blood expression data with dual-view graph convolution network to identify Parkinson’s disease subtypes

Abstract Background As a typical type of neurodegenerative disorders, Parkinson’s disease(PD) is characterized by significant clinical and progression heterogeneity. Based on gene expression data, reliable detection of PACE subtypes in Parkinson’s disease(PD-PACE) has played a crucial role in addres...

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Bibliographic Details
Main Authors: Wei Zhang, Zeqi Xu, Ruochen Yu, Mingfeng Jiang, Qi Dai
Format: Article
Language:English
Published: BMC 2025-08-01
Series:BMC Bioinformatics
Subjects:
Dual-view graph learning
Parkinson-specific gene graphs
Spatiotemporal representations
Whole-blood expression data
Disease subtype detection
Online Access:https://doi.org/10.1186/s12859-025-06181-6
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