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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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-08-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12859-025-06181-6 |
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| Summary: | 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. Results Under the framework of dual-view graph learning, this study proposes a DualGCN-GE method to identify multiple PD-PACE subtypes from whole-blood expression data, with regards of progression velocity. This DualGCN-GE method has proposed dual-view graph convolution network(GCN) to integrate temporal and topological features underlying whole-blood expression data, thus detecting PD-PACE subtypes. Experimental analysis of three benchmark datasets has validated the effectiveness and advantage of the DualGCN-GE method in the disease subtype detection task. Conclusion For gene expression data of human blood samples, topological features have encoded unique information that are absent in temporal features. Using a collaborative fusion strategy, spatio-temporal representations extracted from whole blood expression data have improved accuracy and reliability in detecting PD-PACE subtypes. |
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| ISSN: | 1471-2105 |