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

Journal: BMC bioinformatics
Published Date: Aug 12, 2025

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 addressing the heterogeneity of this disease. Established machine learning approaches generally adopt single-view learning schemes and employ temporal features underlying RNA sequencing data. Topological features, which are associated with gene graphs and cell graphs, were disregarded in previous works. Actually, Parkinson-specific gene graphs(PGG) could act as topological features to capture structural changes of molecular networks.

Authors

  • Wei Zhang
    The First Affiliated Hospital of Nanchang University, Nanchang, China.
  • Zeqi Xu
    College of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou, 310018, Zhejiang, China.
  • Ruochen Yu
    College of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou, 310018, Zhejiang, China.
  • Mingfeng Jiang
    School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, People's Republic of China.
  • Qi Dai
    The First Clinical Medical College, Guangxi University of Chinese Medicine, Nanning 530001, China.

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