MOMHCA-SG: a multi-head cross-attention and similar graph convolutional network framework for Alzheimer’s disease cell type classification
MOMHCA-SG effectively preserves feature integrity during high-dimensional processing, dynamically integrates heterogeneous omics data, and captures cross-modality relationships. These capabilities highlight its potential as a powerful tool for elucidating disease mechanisms and advancing precision medicine in neurodegenerative disorders.
Authors
- Wei Kong (ORCID: https://orcid.org/0000-0002-6554-4338)
- Shuaiqun Wang (ORCID: https://orcid.org/0000-0001-6274-4097)
- Yaling Yu (ORCID: https://orcid.org/0009-0005-7901-8389)
- Gen Wen
- Yan Qian
Institutions
- Shanghai Jiao Tong University (CN)
- Shanghai Sixth People's Hospital (CN)
- Shanghai Maritime University (CN)
Publication Details
- Journal
- Frontiers in Neuroscience
- Published
- 2026-02-16
- DOI
- https://doi.org/10.3389/fnins.2026.1728558
- Primary Topic
- Single-cell and spatial transcriptomics
- Type
- article
- Field-Weighted Citation Impact
- 0.00