All Competitors

Spatial transcriptomics prediction from histology using conditional neural fields to reconstruct continuous gene expression fields.

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

Multi-organ foundation model aligning histology images with spatial-transcriptomics profiles for zero-shot expression and survival prediction.

Virtual spatial transcriptomics foundation model predicting pan-cancer, spatially-resolved single-cell gene expression from H&E histology slides.

Spatial transcriptomics foundation model pairing gene expression with H&E histology for spatial domain discovery and clinical outcome prediction.

Cell-centric microscopy foundation model that distills morphology and microenvironment views into a unified embedding for virtual spatial omics.

Vision-omics finetuning that aligns pathology foundation models with spatial transcriptomics so morphology features predict local gene expression.

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

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.

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.

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

DNA foundation model scaling an encoder-only transformer to 7 billion parameters for variant effect prediction, gene expression, and sequence design.

Chromatin-state language model pretrained on ROADMAP annotations from 127 human cell types to find chromatin-state motifs and predict gene expression.