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 sample appear dissociated to the student.
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.
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
SpatialMOSI provides a critical computational foundation for constructing integrative 3D molecular atlases from complex multimodal spatial data sets and demonstrates SpatialMOSI's versatility across multiple biological systems, accurately identifying spatially conserved domains, imputing missing omic layers, and recons...
Peimeng Zhen, Han Shu, Bingtao Wang et al.· Genome Research· 0 citations
This work proposes BERT for Spatial Transcriptomics (BertST), a transformer-based framework that reformulates spatial transcriptomics as a graph-to-text representation learning problem, and constructs a task-specific multi-graph representation integrating spatial adjacency, pruned gene-expression similarity, and a full...
A novel method of multimodal spatial omics fusion, named mmspao, is proposed to obtain cross-modal interactive features and combine them with the features of each modality to obtain fusion results at the spatial resolution.
AINR is an end-to-end deep learning framework that models spatial transcriptomics data as a geometrically constrained continuous biological field, using a periodic sine activation function to map spatial coordinates directly to gene expression while preserving spatial smoothness without explicit adjacency matrices.
Yu-Sen Zhang, Guo-Dong Xiao, Po-Nian Li et al.· Bioinformatics Advances· 0 citations
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