All Competitors

A VAE trained on scRNA-seq reference data and applied frozen at inference to impute unmeasured genes and denoise spatial transcriptomics profiles.

Multimodal foundation model pretrained on 1.76 billion paired histology-spatial transcriptomics spots, linking whole-slide images to spatial molecular programs.

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 tri-modal foundation model unifying histology images, spatial transcriptomics, and biological language for zero-shot spatial biology and pathology reasoning.

A diffusion transformer that synthesizes H&E histopathology image patches conditioned jointly on spatial transcriptomics gene expression and morphological embeddings.

Geometry-first generative framework that reconstructs single-cell spatial coordinates by integrating scRNA-seq with spatial transcriptomics, without cell-type labels.

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.

A foundation model that predicts spatial transcriptomics and proteomics directly from routine H&E whole-slide images using a vision transformer aligned with a language model.

A transcriptomics-native single-cell foundation model that couples batch-invariant representation learning with probabilistic virtual-cell generation.

A pretrained whole-cell segmentation model for spatial transcriptomics that fuses DAPI nuclear images with RNA transcript density to recover cell boundaries.

A generative diffusion-transformer foundation model that embeds H&E histology, RNA profiles, and clinical text in a shared latent space for zero-shot cross-modal synthesis.

A hierarchical multimodal foundation model integrating spatial transcriptomics and H&E histology for biological discovery and platform-agnostic clinical prediction.

Lightweight multimodal foundation model integrating spatial transcriptomics and H&E histopathology with pathway activity scores for biologically grounded spatial niche discovery at single-cell resolution.

A self-supervised, cell-centric pretraining strategy that distills morphology and microenvironment views of each cell into a unified embedding for virtual spatial omics from microscopy.

A pan-cancer pretrained latent diffusion model that enhances spatial transcriptomics, imputing genome-wide expression from sparse panels with zero-shot generalization.

A pan-cancer generative model that predicts spatial gene expression from H&E histology using conditional flow matching with a mixture-of-experts velocity field.

A lightweight graph-convolutional foundation model for spatial transcriptomics that learns spatially coherent, interpretable spot embeddings via masked central-spot prediction.

A foundation model jointly pretrained on 67M single-cell and spatial transcriptomic profiles to model intra-cellular expression and inter-cellular spatial dependencies.

A pair of spatially aware transcriptomic foundation models (50um-Local and 250um-Extended) for multi-scale analysis of spot-resolution cancer spatial transcriptomes.

Generative foundation model that enhances spatial transcriptomics by conditioning on H&E histology, scRNA-seq references, and spatial co-expression priors.

A conversational single-cell and spatial multi-omics brain agent pretrained on 130 million cells across species for zero-shot cell annotation and disease prediction.