All Competitors

Multimodal foundation model for precision neurology that reconstructs a patient's molecular brain state from blood to predict disease progression.

BERT-style language model for somatic mutations, pretrained on cancer sequencing from 210,000+ patients for tumor subtyping and therapy response.

Cross-species multimodal foundation model of immunology and inflammation, harmonizing transcriptomics and histology into patient-level embeddings.

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

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

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

Pretrained variational autoencoder for untargeted blood metabolomics, learning metabolite representations that transfer to clinical prediction tasks.

Spatial proteomics foundation model that embeds every marker through its protein sequence, so heterogeneous antibody panels share one representation.

Spatial virtual-cell foundation model that simulates single-cell gene expression at chosen positions in real human tumor tissue.

Patient-level foundation model that pools every cell in an scRNA-seq sample into one disease representation, trained on 24.3 million cells.

Patient-level single-cell foundation model that condenses a donor's scRNA-seq profile into one 288-dimensional embedding for disease cohort search.