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.
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-cel...
Y. Ekiz, E. Koç, Aykut Koç· Signal Processing and Commun...· 0 citations
LATTICE demonstrated stable optimization behavior, reproducible embeddings across analysis seeds, and complete multimodal integration across all samples, position LATTICE as a practical and empirically grounded framework for multimodal spatial omics integration, while also highlighting the need for stronger supervision...
J. Dwarampudi, V. Kochat, Suresh Satpati et al.· 0 citations
It is demonstrated that DAHGT-CCI can more accurately reconstruct cell communication networks in complex tissue microenvironments, offering an indispensable computational tool for studying developmental processes, disease mechanisms, and potential therapeutic targets from a spatially resolved perspective.
Wei-Liang Huo, Shuo Yu, Qing-Chen Zhang· Proceedings of the Thirty-Fi...· 0 citations
SaDGAE, an unsupervised deep graph autoencoder (GAE) framework that jointly models gene expression patterns and cell–cell relationships, achieves strong and competitive clustering performance, yielding biologically interpretable clusters and accurately recovering known marker gene patterns.
Xiang-Hui Liu, Yanmei Hu, Sheng-Lin Yang et al.· International Journal of Dat...· 0 citations
SpatialJEPA is introduced, a JEPA-inspired teacher–student framework for transferring spatial context from spatial multiomics data to non-spatial multiome data, which masks spatial context by replacing the teacher’s spatial neighborhood graph with a self-only identity graph during student training, making the spatial s...
E evaluation across diverse biological systems shows that SGFN achieves improved or competitive performance relative to representative baseline methods in reference-based benchmarks, and identifies biologically coherent spatial or functional regions in unlabeled datasets supported by marker-gene, spatial-autocorrelatio...
