DualGCN-GE: integration of spatiotemporal representations from whole-blood expression data with dual-view graph convolution network to identify Parkinson’s disease subtypes
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.
Authors
- Mingfeng Jiang (ORCID: https://orcid.org/0000-0003-3013-4790)
- Qi Dai (ORCID: https://orcid.org/0000-0003-2675-6511)
- Wei Zhang (ORCID: https://orcid.org/0000-0002-7622-6714)
- Zeqi Xu
- Ruochen Yu
Institutions
- Zhejiang Sci-Tech University (CN)
Publication Details
- Journal
- BMC Bioinformatics
- Published
- 2025-08-12
- DOI
- https://doi.org/10.1186/s12859-025-06181-6
- Citations
- 1
- Primary Topic
- Gene expression and cancer classification
- Type
- article
- Field-Weighted Citation Impact
- 0.81
Funders
- National Natural Science Foundation of China
- National Key Research and Development Program of China
- Natural Science Foundation of Zhejiang Province