Spatial omics Models
Spatial omics foundation models operate on measurements that retain their physical coordinates within a tissue — spatial transcriptomics, histology-to-expression prediction, and models of the tumor and tissue microenvironment. Unlike dissociated single-cell data, spatial models learn how neighboring cells influence one another and how gene expression is organized across tissue architecture. By preserving the where alongside the what, they are revealing the structural logic of development, immunity, and disease at cellular resolution.
8 models in this category
Notable Models
Top-rated spatial omics models from our evaluations
Multimodal tumor foundation model trained on paired H&E histology and spatial transcriptomics to infer whole-transcriptome and tumor-microenvironment signal from routine H&E alone.
A diffusion transformer that synthesizes H&E histopathology image patches conditioned jointly on spatial transcriptomics gene expression and morphological embeddings.
A spatial-transcriptomics foundation model for the tumor microenvironment that produces TME-aware embeddings and enables in silico perturbation from a fixed pretrained checkpoint.
A transferable spatial-transcriptomics deconvolution model whose rank-based spot encoding lets one pretrained model generalize across tissues, disease states, and platforms without retraining.
A multi-organ foundation model aligning histology image features with spatial-transcriptomics gene expression across 13 organs for zero-shot virtual ST and survival prediction.
Latent flow-matching foundation model that predicts pan-cancer spatially-resolved single-cell gene expression directly from routine H&E histology slides.