Jul 2026· Signal Processing and Communications Applications Conference· pp. 1-4· 0 citations· 17 references
Abstract
Graph neural networks (GNNs) provide an effective mechanism for enhancing transformer representations by modeling relational structures that sequence-based models cannot directly capture. In this study, a multiview graph neural network enhanced transformer architecture is proposed for cell-type annotation in single-cell RNA sequencing (scRNA-seq) data. The proposed approach models complementary relationships between cells using multiple graph views constructed from the training expression matrix, allowing the GNN component to refine transformer-derived embeddings through neighborhood-based information propagation. By combining transformer-based contextual representations with relational information from multiple graph structures, the framework exploits both structural and feature-level patterns in scRNA-seq data. Experimental results show that the proposed method improves cell-type annotation performance compared with existing approaches.
Experiments demonstrate that the GraphFusionNN fusion strategy— integrating graph topology, spatial context, and external embeddings—significantly improves classification accuracy, macro-F1, and robustness compared to single-modality models.
A structure-aware interleaved-attention graph learning framework, termed IAGRN, is proposed for GRN inference from scRNA-seq data that interleaves topology-constrained local attention with distance-aware global attention, enabling effective integration of structural priors and long-range regulatory signals.
Yue Wang, Si-Cheng Tian, Dan Li· International Journal of Mol...· 0 citations
Despite the rapid progress of single-cell RNA sequencing (scRNA-seq), accurate cell type annotation remains a major challenge. Existing approaches often struggle with sparse and heterogeneous expression profiles, insufficient genelevel modeling, and complications such as zero inflation and class imbalance. To address these issues, we propose scGFormer (Single-cell Multi-scale Graph Transformer), a unified framework that integrates: (i) Performer-based Global Attention (PGA) to capture long-range dependencies, (ii) Graph-based Local Attention (GLA) to model neighborhood structures, and (iii) a Squeeze-and-Excitation Gene Reweighting module (GeneSE) to enhance gene-level representations. Furthermore, scGFormer is equipped with a biology-guided adaptive contrastive learning strategy, which is designed to account for zero inflation, balance class distributions, and refine dynamic graphs during training, thereby facilitating robustness and adaptability. By explicitly modeling both global and local dependencies while strengthening gene-level representations, scGFormer achieves improved robustness and generalization. Extensive experiments across public datasets demonstrate that scGFormer achieves competitive or superior performance compared with state-of-theart methods, offering a robust solution for single-cell annotation across diverse datasets and species. Our code is publicly available at https://github.com/wuzi11/scGFormer.
Unknown authors· IEEE transactions on computa...· 0 citations
Similarity-guided Structural Matching Learning for Graph Dataset Condensation (SSGDC) is proposed, which efficiently reduces repository size while maintaining both task performance and structural information.
Yiyang Zhang, Yutong Ye, Yingbo Zhou et al.· 0 citations
Experiments on three cross-patient scRNA-seq data sets demonstrate that PathoGraph achieves stable annotation performance across 32 directed reference-to-query transfer tasks, showing competitive and stable performance compared with representative marker-based, correlation-based, and model-based annotation methods.
Yue C. Li, Mengmeng Wei, Xinfei Wang et al.· Journal of Chemical Informat...· 0 citations
Edge Generation-guided Relation-aware Learning (EGRL) is proposed, a novel framework with several key components: implicit meta-path learning to capture relational semantics without handcrafted paths; a multi-relation-aware attention mechanism for adaptive fusion of interaction patterns; a graph generator that predicts potential ("soft") edges to support cold-start nodes; and a multi-feature fusion predictor for final interaction scoring.
Danyu Li, Ling Zhou, Rubing Huang et al.· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
