All Competitors

Spatially aware transcriptomic foundation models for cancer, pairing 50um-Local and 250um-Extended views of spot-resolution spatial transcriptomes.

Generative foundation model that imputes genes and denoises spatial transcriptomics, conditioned on H&E histology, scRNA-seq, and spatial priors.

Multimodal foundation model that embeds histology, transcriptomics, and clinical records in one space for patient stratification and target discovery.

Pathology foundation model that aligns whole-slide images with genomic, epigenetic, and transcriptomic data for patient-level tumor representations.

Tissue imaging foundation model pretrained on matched H&E histology and spatial proteomics for cross-modal inference and zero-shot retrieval.

Spatial transcriptomics language model that reads tissue as spatial sentences to simulate cell profiles and run in silico perturbations.

Generative framework that reconstructs missing spatial transcriptomics regions by jointly predicting cell locations, cell types, and gene expression.

Multi-modal contrastive model that aligns H&E histopathology with spatial transcriptomics across tissue scales to predict gene expression from images.

Multimodal foundation model that distills Evo 2 into a compact encoder guided by Hi-C data, predicting cell-type-specific 3D genome architecture.

Histopathology model that predicts single-cell type composition and reconstructs spatial gene expression from H&E slides, with no molecular assay.

Vision Transformer foundation model for spatial metabolomics, pretrained on ~4,000 curated METASPACE mass spectrometry imaging datasets.

Kidney-specialized single-cell foundation model trained across four mammalian species for zero-shot cell-type annotation and batch integration.

Predicts virtual single-cell spatial transcriptomics from H&E histology using frozen pathology foundation models and spot-level supervision.

Histology vision transformer with 80M parameters that predicts spatial gene expression from H&E tissue images and transfers to tumor detection.

Spatial proteomics foundation model, marker-aware and panel-agnostic, pretrained on 47 million multiplexed tissue-imaging patches from 175 markers.

Patch-based 3D diffusion model that generates teravoxel-scale virtual mouse brain volumes conditioned on spatially resolved gene expression.

Histology-anchored framework pairing an H&E foundation model with a cellular hypergraph to predict single-cell multi-omics from tissue images.

Cell phenotyping model for spatial proteomics using a language-informed vision transformer to classify cell types zero-shot across marker panels.