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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BMC
2025-08-01
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| Series: | BMC Bioinformatics |
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| Online Access: | https://doi.org/10.1186/s12859-025-06181-6 |
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| author | Wei Zhang Zeqi Xu Ruochen Yu Mingfeng Jiang Qi Dai |
| author_facet | Wei Zhang Zeqi Xu Ruochen Yu Mingfeng Jiang Qi Dai |
| author_sort | Wei Zhang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-d3de941ab9854e9ba3badbaebf75dda6 |
| institution | DOAJ |
| issn | 1471-2105 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj-art-d3de941ab9854e9ba3badbaebf75dda62025-08-20T03:06:36ZengBMCBMC Bioinformatics1471-21052025-08-0126111810.1186/s12859-025-06181-6DualGCN-GE: integration of spatiotemporal representations from whole-blood expression data with dual-view graph convolution network to identify Parkinson’s disease subtypesWei Zhang0Zeqi Xu1Ruochen Yu2Mingfeng Jiang3Qi Dai4College of Computer Science and Technology, Zhejiang Sci-Tech UniversityCollege of Computer Science and Technology, Zhejiang Sci-Tech UniversityCollege of Computer Science and Technology, Zhejiang Sci-Tech UniversityCollege of Computer Science and Technology, Zhejiang Sci-Tech UniversityCollege of Life Science and Biomedicine, Zhejiang Sci-Tech UniversityAbstract 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.https://doi.org/10.1186/s12859-025-06181-6Dual-view graph learningParkinson-specific gene graphsSpatiotemporal representationsWhole-blood expression dataDisease subtype detectionParkinson-specific gene graphs |
| spellingShingle | Wei Zhang Zeqi Xu Ruochen Yu Mingfeng Jiang Qi Dai DualGCN-GE: integration of spatiotemporal representations from whole-blood expression data with dual-view graph convolution network to identify Parkinson’s disease subtypes BMC Bioinformatics Dual-view graph learning Parkinson-specific gene graphs Spatiotemporal representations Whole-blood expression data Disease subtype detection Parkinson-specific gene graphs |
| title | DualGCN-GE: integration of spatiotemporal representations from whole-blood expression data with dual-view graph convolution network to identify Parkinson’s disease subtypes |
| title_full | DualGCN-GE: integration of spatiotemporal representations from whole-blood expression data with dual-view graph convolution network to identify Parkinson’s disease subtypes |
| title_fullStr | DualGCN-GE: integration of spatiotemporal representations from whole-blood expression data with dual-view graph convolution network to identify Parkinson’s disease subtypes |
| title_full_unstemmed | DualGCN-GE: integration of spatiotemporal representations from whole-blood expression data with dual-view graph convolution network to identify Parkinson’s disease subtypes |
| title_short | DualGCN-GE: integration of spatiotemporal representations from whole-blood expression data with dual-view graph convolution network to identify Parkinson’s disease subtypes |
| title_sort | dualgcn ge integration of spatiotemporal representations from whole blood expression data with dual view graph convolution network to identify parkinson s disease subtypes |
| topic | Dual-view graph learning Parkinson-specific gene graphs Spatiotemporal representations Whole-blood expression data Disease subtype detection Parkinson-specific gene graphs |
| url | https://doi.org/10.1186/s12859-025-06181-6 |
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